Publications

@proceedings{DBLP:conf/europar/2026-1,
  editor       = {Massimo Torquati and
                  Gabriele Mencagli and
                  Valeria Cardellini and
                  Alessia Antelmi and
                  Doriana Medic},
  title        = {Euro-Par 2026: Parallel Processing - 32nd European Conference on Parallel
                  and Distributed Processing, Pisa, Italy, August 24-28, 2026, Proceedings,
                  Part {I}},
  series       = {Lecture Notes in Computer Science},
  volume       = {16781},
  publisher    = {Springer},
  year         = {2027},
  url          = {https://doi.org/10.1007/978-3-032-35248-4},
  doi          = {10.1007/978-3-032-35248-4},
  isbn         = {978-3-032-35247-7},
  timestamp    = {Mon, 24 Aug 2026 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/europar/2026-1.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/europar/2026-2,
  editor       = {Massimo Torquati and
                  Gabriele Mencagli and
                  Valeria Cardellini and
                  Alessia Antelmi and
                  Doriana Medic},
  title        = {Euro-Par 2026: Parallel Processing - 32nd European Conference on Parallel
                  and Distributed Processing, Pisa, Italy, August 24-28, 2026, Proceedings,
                  Part {II}},
  series       = {Lecture Notes in Computer Science},
  volume       = {16782},
  publisher    = {Springer},
  year         = {2027},
  url          = {https://doi.org/10.1007/978-3-032-35251-4},
  doi          = {10.1007/978-3-032-35251-4},
  isbn         = {978-3-032-35250-7},
  timestamp    = {Mon, 24 Aug 2026 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/europar/2026-2.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{bifulco2026parallelqpu,
  author    = {Mario Bifulco and Luca Roversi},
  title     = {Optimizing the Execution Strategy of Node-Local Quantum Decision Trees via Circuit Batching},
  booktitle = {Accepted for publication in: Euro-Par 2026: Parallel Processing Workshops},
  series    = {Lecture Notes in Computer Science},
  volume    = {17119},
  publisher = {Springer},
  address   = {Pisa, Italy},
  year      = {2026},
  note      = {1st Workshop on Quantum--HPC Workflows, Resources Allocation, and Scheduling (Q-WORKS 2026), Pisa, Italy, August 24--28, 2026}
}

@inproceedings{bifulco2026qhypergraph,
  author    = {Mario Bifulco and Doriana Medi{\'c} and Alessia Antelmi and Marco Aldinucci},
  title     = {Hypergraph Clustering via Quantum Optimization},
  booktitle = {Accepted for publication in: Optimization and Decision Science Conference 2026: Book of Abstracts},
  year      = {2026},
  pages     = {125},
  address   = {Galzignano Terme, Italy},
  note      = {Abstract ID: ODS-123, Session S11 -- Quantum Optimization}
}

@article{Birke2026TMPDiff,
  author        = {Lewandowski, Basile and Kurz, Simon and Shankar, Aditya and Birke, Robert and Chen, Jian-Jia and Chen, Lydia Y.},
  title         = {{TMPDiff}: Temporal Mixed-Precision for Diffusion Models},
  journal       = {arXiv preprint arXiv:2603.14062},
  year          = {2026},
  eprint        = {2603.14062},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CV},
  doi           = {10.48550/arXiv.2603.14062},
  url           = {https://arxiv.org/abs/2603.14062}
}

@article{Antelmi2026PoliticalPersuasion,
  author        = {Antelmi, Alessia and Galdeman, Alessia and La Cava, Lucio and Pera, Arianna and Da San Martino, Giovanni},
  title         = {Political Persuasion and Endorsement in Large Language Models},
  journal       = {arXiv preprint arXiv:2606.05961},
  year          = {2026},
  eprint        = {2606.05961},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CL},
  doi           = {10.48550/arXiv.2606.05961},
  url           = {https://arxiv.org/abs/2606.05961}
}

@inproceedings{Scozzaro2026KenjiEndo,
  author    = {Scozzaro, Calogero Jerik and Rinaldi, Matteo and Mittone, Gianluca and Stranisci, Marco Antonio},
  title     = {{Kenji-Endo}: a {BabyLM} @{EVALITA}},
  booktitle = {Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop ({EVALITA} 2026)},
  pages     = {16--26},
  address   = {Bari, Italy},
  publisher = {CEUR Workshop Proceedings},
  year      = {2026},
  month     = feb,
  isbn      = {979-12-243-0586-6},
  url       = {https://aclanthology.org/2026.evalita-1.4/}
}

@inproceedings{Vaccaro2026DigitalTwins,
	abstract = {Digital Twins are increasingly adopted to support the operational management of complex
and energy-intensive infrastructures, yet their practical implementation often remains
fragmented across heterogeneous platforms and proprietary monitoring systems. In data
center environments, this fragmentation limits the interpretability of real-time data and
weakens the connection between operational measurements, spatial context, and asset-
level decision-making. This paper presents a BIM-based Digital Twin architecture developed
for the HPC4AI (High-Performance Computing for Artificial Intelligence) data center, hosted
at the University of Turin's Computer Science Department, within the framework of the
DYMAN project, which aims to develop advanced solutions to reduce the impact of thermal
energy management in high-performance computing installations and data server rooms.
The proposed approach integrates real-time IoT monitoring with Building Information
Modelling through a modular layered architecture that decouples sensing, time-series data
management, and digital representation. Sensor data are stored in a time-series database
and synchronized into the BIM environment via automated Dynamo workflows and
embedded Python scripts, where shared parameters enable semantic mapping to building
elements. This supports near real-time spatial contextualization, rule-based alerting, and
native BIM visualisation without reliance on external dashboards. The case study
demonstrates how the BIM model evolves from a static geometric artifact into an
operational interface capable of supporting situational awareness, diagnostics, and
integration with predictive analytics and energy optimization strategies. The paper discusses
system architecture, data synchronization mechanisms, and transferability to other
mission-critical infrastructures, highlighting the role of data-driven integration strategies in
advancing scalable and interoperable Digital Twins for smart and sustainable built
environments.},
	address = {Cambridge, UK},
	author = {Vaccaro, Viviana and Tagliabue, Lavinia Chiara and Birke, Robert and Aldinucci, Marco and Meschini, Silvia},
	booktitle = {Proc. of Smart and Sustainable Built Environment Conference Series},
	date-modified = {2026-09-01 23:50:08 +0200},
	issn = {2979-1804},
	keywords = {dyman, hpc4ai, DCs},
	month = {jul},
	pages = {1-12},
	title = {Digital Twins for Mission-Critical Infrastructures: A {BIM}-Based Framework for Real-Time Monitoring and Decision Support},
	url ={https://dyman-project.eu/download/digital-twins-for-mission-critical-infrastructures-a-bim-based-framework-for-real-time-monitoring-and-decision-support/?wpdmdl=10085&refresh=6a974a29bb57b1788299817},
	year = {2026}}

@techreport{26:etp4hpc:data-platform,
	abstract = {This is a white paper released as part of the ETP4HPC's Strategic Research Agenda 6. This White Paper argues for the need for a renewed focus on Storage and I/O needed in Europe, which has been largely ignored despite the critical importance of storage and data management for AI applications and Large Language Models. The paper presents the need for building a European Data Platform suitable for Exascale HPC and AI whilst keeping the needs of European technological sovereignty firmly in context. The paper presents technical directions and policy recommendations for the above.},
	author = {Jean-Thomas Acquaviva and Andr{\'e} Brinkmann and Jos{\'e} Noudohouenou and George Markomanolis and Jes{\'u}s Carretero and Javier Garc{\'\i}a-Blas and Marco Aldinucci and Felix Wolf and Erwin Laure and Sai Narasimhamurthy and Angelos Bilas and Philippe Deniel and Martin Barisits and Bryan Lawrence and Simon Smart and Val{\'e}ry Guilleaume},
	date-modified = {2026-08-16 11:38:32 +0200},
	doi = {10.5281/zenodo.19456172},
	institution = {ETP4HPC},
	keywords = {hpc, admire, IT4LIA, ICSC},
	month = apr,
	note = {European Technology Platform for High Performance Computing (ETP4HPC)},
	title = {European Data Platform for HPC and AI: A Path to Data Sovereignty},
	type = {SRA 6 White Paper},
	url = {https://doi.org/10.5281/zenodo.19456172},
	year = {2026},
	bdsk-url-1 = {https://doi.org/10.5281/zenodo.19456172}}

@inproceedings{Tagliabue2026EnhancingDatacenter,
	abstract = {Digital technologies are transforming how we manage and repurpose the built environment, enhancing energy performance, system reliability, and user wellbeing. Among these innovations, datacenters stand out as essential infrastructures due to their scale, criticality, and intensive energy usage. Traditionally housed in purpose-built facilities, datacenters are increasingly being considered for integration into existing
	buildings, including those of historical or industrial value. This adaptive reuse offers an opportunity to reconcile heritage conservation with contemporary needs for connectivity, sustainability, and digital resilience. This paper presents two case studies exploring the integration of digital infrastructure into a historic building and a newly constructed facility. Both projects address key challenges related to energy efficiency, spatial constraints, and architectural adaptation. Emphasis is placed on strategies for heat recovery, enabling the reuse of waste heat for other compatible functions, and on the deployment of Digital Twins to simulate, monitor, and optimize performance in real time. These examples demonstrate how datacenters can catalyze sustainable regeneration while maintaining the cultural value of heritage sites. By aligning digital innovation with architectural reuse, the projects highlight a replicable model for embedding AI-driven systems into the future of built heritage.},
	address = {Rome, Italy},
	author = {Tagliabue, Lavinia Chiara and Aldinucci, Marco and Vaccaro, Viviana and Meschini, Silvia and Birke, Robert},
	booktitle = {Proceedings of the Stati Generali del Patrimonio Industriale 2026},
	date-modified = {2026-08-14 12:33:52 +0200},
	editor = {Curr{\`a}, Edoardo and Fatiguso, Fabio and Guida, Antonella and Monte, Antonio and Docci, Marina and Bernardo, Graziella and Cantatore, Elena and Mattioli, Alessandro and Menichelli, Claudio},
	isbn = {978-88-492-5487-7},
	keywords = {DCs, dyman},
	publisher = {Gangemi Editore},
	title = {Enhancing Datacenter Integration in the Built Environment through Digital Twins},
	url = {https://iris.unito.it/handle/2318/2124617},
	year = {2026},
	bdsk-url-1 = {https://iris.unito.it/handle/2318/2124617}}

@inproceedings{11626216,
	abstract = {The rapid expansion of data centers driven by cloud computing, high performance computing, and artificial intelligence has intensified the need for reliable frameworks to assess and communicate their environmental sustainability. Among existing tools, the Leadership in Energy and Environmental Design (LEED) certification has emerged as the most widely adopted green building rating system applied to data centers. However, LEED was originally developed for conventional building typologies, raising questions regarding its adequacy for capturing the specific environmental characteristics of data center facilities. This paper critically examines the application of LEED certification to data centers, focusing on LEED O+M: Data Centers (v4). The study combines a review of official USGBC guidance with a comparative analysis of multiple LEED-certified data center scorecards awarded between 2015 and 2024. The distribution of points across certification categories is analyzed to identify recurring patterns, dominant credit areas, and structural limitations. The results show that LEED certification for data centers is strongly driven by operational energy performance, with the Energy & Atmosphere category consistently accounting for the largest share of awarded points in Gold and Platinum projects. Credits related to commissioning, energy monitoring, and performance optimization play a decisive role in achieving high certification levels. Conversely, categories addressing materials, resource use, and long-term environmental impacts remain marginal, while occupant-centric criteria retain significant weight despite limited relevance to low-occupancy data center environments. The findings indicate that LEED is an effective framework for operational sustainability benchmarking and governance in data centers but provides a partial representation of their overall environmental performance. The paper concludes by discussing the need for integrating certification frameworks with complementary analytical approaches capable of addressing life-cycle impacts and technological dynamics, thereby enhancing the robustness of sustainability assessment for data centers.},
	author = {Tagliabue, Lavinia Chiara and Vaccaro, Viviana and Zichi, Alessandro and Birke, Robert and Aldinucci, Marco and Meschini, Silvia},
	booktitle = {2026 34th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},
	date-modified = {2026-08-14 12:10:40 +0200},
	doi = {10.23919/PDPCPS00030.2026.00047},
	keywords = {DCs, dyman},
	month = {March},
	pages = {300-307},
	publisher = {{IEEE}},
	title = {LEED Certification and Data Centers: A Critical Assessment of Building Sustainability Rating Systems for HPC Facilities},
	year = {2026},
	bdsk-url-1 = {https://doi.org/10.23919/PDPCPS00030.2026.00047}}

@inproceedings{11626207,
	abstract = {High-performance computing (HPC) and artificial intelligence (AI) data centers are increasingly recognized as critical infrastructures for scientific research, yet they are also associated with significant environmental burdens due to their high energy intensity and rapid technological obsolescence. Traditional performance metrics such as Power Usage Effectiveness (PUE) primarily capture operational efficiency and fail to account for embodied impacts related to construction, equipment manufacturing, and component replacement. This study presents a preliminary Life Cycle Assessment (LCA) of the HPC4AI data center at the University of Turin, conducted in accordance with ISO 14040--44 [1] and structured using the EN 15978 modular framework. The assessment adopts a screening-level, attributional LCA with a 30-year reference study period, integrating primary inventory data derived from seven detailed design and equipment schedules covering architectural elements, IT infrastructure, electrical systems, lighting, and cooling equipment. Life-cycle impacts are quantified for the modules A1--A3 (product stage), B4 (replacement due to technological obsolescence), and B6 (operational energy use), while other modules are structurally included as placeholders. Global warming potential (GWP) is used as the impact indicator, with multiple electricity mix scenarios applied to evaluate operational sensitivity. Results indicate that operational electricity use (Module B6) is the dominant contributor to life-cycle GWP, confirming findings from recent HPC and AI data center LCAs. However, embodied impacts associated with IT and cooling equipment manufacturing and replacement (A1--A3 + B4) represent a substantial secondary contribution, with replacement impacts of comparable magnitude to initial construction. A component-level ranking reveals that servers, UPS systems, and core cooling equipment are the principal sources of embodied emissions, while architectural components contribute marginally. The study highlights the critical role of electricity decarbonization, hardware lifetime management, and circular strategies in reducing the environmental footprint of academic HPC facilities. Despite its screening nature, the LCA provides a robust baseline for future refined assessments and supports the integration of life-cycle thinking into the design and governance of research-oriented data centers.},
	author = {Tagliabue, Lavinia Chiara and Vaccaro, Viviana and Zichi, Alessadro and Birke, Robert and Aldinucci, Marco},
	booktitle = {2026 34th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},
	date-modified = {2026-08-14 12:10:48 +0200},
	doi = {10.23919/PDPCPS00030.2026.00045},
	keywords = {DCs, dyman},
	month = {March},
	pages = {285-292},
	publisher = {{IEEE}},
	title = {Preliminary Life Cycle Assessment of a HighPerformance Computing Data Center: A Case Study of the HPC4AI Facility at the University of Turin},
	year = {2026},
	bdsk-url-1 = {https://doi.org/10.23919/PDPCPS00030.2026.00045}}

@inproceedings{11626213,
	abstract = {Data centers (DCs) represent one of the most energy-intensive and operationally critical categories of buildings, supporting essential digital services such as cloud computing, large-scale data storage, and artificial intelligence applications. Their operation is characterized by complex interdependencies between information technology loads, cooling infrastructures, power distribution systems, and environmental conditions. Ensuring efficiency, reliability, and resilience under these constraints remains a significant challenge for operators and designers alike. In recent years, the concept of Digital Twin (DT) has emerged as a promising paradigm to address this complexity by enabling a continuously updated digital representation of physical systems, enriched with real-time data and analytical capabilities. Despite increasing attention in both academia and industry, DT solutions for buildings and DCs remain largely fragmented. Existing approaches often focus on individual aspects such as visualization, simulation, or monitoring dashboards, while failing to provide robust integration between design data, operational measurements, and control systems. A recurring limitation concerns the lack of interoperability between Building Information Modeling (BIM) data and Internet of Things (IoT) infrastructures, which prevents the construction of coherent and scalable DT systems. This paper proposes a reference architecture for DT applied to DCs, grounded in the integration of BIM-based representations, real-time IoT data acquisition, analytical models, and control mechanisms. The architecture adopts a layered perspective, separating concerns related to data management, semantic integration, modeling, analytics, and actuation, while maintaining consistency across these layers. Informed by recent reviews on building-scale DT, the proposed approach addresses the well-known bottlenecks associated with BIM usage and data acquisition. The contribution of this work lies in providing an architectural blueprint able to support interoperability and reuse, offering a structured foundation for the development of advanced DT solutions in DC environments, and complemented by practical metrics that make DT implementations assessable beyond project-specific prototypes.},
	author = {Meschini, Silvia and Gasbarri, Paola and Tagliabue, Lavinia Chiara and Vaccaro, Viviana and Birke, Robert and Aldinucci, Marco},
	booktitle = {34th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing {(PDP)}},
	date-modified = {2026-08-14 12:10:56 +0200},
	doi = {10.23919/PDPCPS00030.2026.00048},
	keywords = {DCs, dyman},
	pages = {308-315},
	publisher = {{IEEE}},
	title = {Digital Twin for DCs: a Reference Architecture Based On BIM, IoT and Analytics Control},
	year = {2026},
	bdsk-url-1 = {https://doi.org/10.23919/PDPCPS00030.2026.00048}}

@article{2026:micpro:dare,
	abstract = {Driven by the need of technological sovereignty, the European Union has accelerated the development of a sustainable High-Performance Computing (HPC) ecosystem. In this perspective, the DARE SGA1 (Digital Autonomy with RISC-V in Europe) project seeks to decouple European infrastructure from proprietary, non-European hardware by developing high-performance processors and computing accelerators based on the open-source RISC-V architecture. This paper introduces and reports about the current status of the multifaceted contributions of Italian partners to the critical pathfinding activities within the DARE SGA1 framework aiming at closing the gap between current prototypes and future HPC systems as well as establishing a bridge towards a fully independent European supercomputing supply chain. In particular, the activities focus on (i) novel RISC-V-based accelerator designs optimized for energy efficiency and high throughput and scaling-out capability; (ii) improvement of RISC-V-based system reliability and resiliency; (iii) evolution of the software stack for low level software and compiler optimizations (iv) co-design through benchmarking with AI and Quantum Chemistry scalable applications.},
	author = {Giovanni Agosta and Marco Aldinucci and Marcello Barbirotta and Andrea Bartolini and Laura Bellentani and Andrea Biagioni and Daniele Cattaneo and Daniele Cesarini and Carlotta Chiarini and Iacopo Colonnelli and Francesco Conti and Pietro Delugas and Lev Denisov and Ottorino Frezza and Angelo Garofalo and Marco Grangetto and Juan David Guerrero Balaguera and Francesca {Lo Cicero} and Alessandro Lonardo and Michele Martinelli and Andrea Maslov and Francesco Menichelli and Mauro Olivieri and Pierpaolo Perticaroli and Luca Pontisso and Cristian Rossi and Josie Esteban Rodriguez Condia and Daniele Rossi and Davide Rossi and Sergio Saponara and Antonio Sciarappa and Francesco Simula and Matteo Sonza Reorda and Gabriele Spadaro and Massimo Torquati and Piero Vicini},
	doi = {10.1016/j.micpro.2026.105321},
	issn = {0141-9331},
	journal = {Microprocessors and Microsystems},
	keywords = {dare, streamflow, jupyter-workflow, capio},
	pages = {105321},
	title = {The Italian pathfinding strategy for {RISC-V}-based {HPC}: Progress within {DARE-SGA1} project},
	url = {https://iris.unito.it/retrieve/e6b5b8a2-a130-4123-9f3b-22f2befeadd0/DARE_ICSC_IEEE_MICPRO.pdf},
	volume = {124},
	year = 2026,
	bdsk-url-1 = {https://iris.unito.it/retrieve/e6b5b8a2-a130-4123-9f3b-22f2befeadd0/DARE_ICSC_IEEE_MICPRO.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.micpro.2026.105321}}

@article{2026:fgcs:qpu,
	abstract = {As quantum computing (QC) technologies mature, their integration into established high-performance computing (HPC) infrastructures is becoming a central objective for next-generation computing systems. However, unlocking the potential of hybrid platforms for computationally demanding workloads remains challenging. The mismatch between quantum and classical programming models, the limited maturity of quantum software stacks, and the scarcity of quantum processing units (QPUs) above all, necessitate scheduling strategies that go beyond standard HPC mechanisms to manage such heterogeneous and constrained resources. To address this issue, we investigate three distinct methodologies for HPC-QC resource scheduling: time-based multiplexing, dynamic resource management, and workflow decomposition. Experimental validation on production HPC clusters and real quantum hardware demonstrates the effectiveness of these approaches under different workload scenarios. Malleability and workflow strategies significantly optimise classical resource utilisation, reducing consumption by up to 45.7\% and 64\% respectively, proving to be best fitted for hybrid jobs where quantum and classical workloads are evenly balanced. Conversely, time-multiplexing enhances QPU utilisation and reduces execution time at the cluster level, making it the optimal strategy for the opposite context, which is characterised by high classical-quantum workload imbalances. These findings underscore the practical viability of tailored scheduling strategies for hybrid HPC-QC environments and highlight their complementarity in building efficient, scalable software stacks for next-generation quantum-accelerated facilities.},
	author = {Marco Cipollini and Simone Rizzo and Sergio Iserte and Paolo Viviani and Giacomo Vitali and Matteo Barbieri and Gabriella Bettonte and Elisabetta Boella and Fulvio Ganz and Roberto Rocco and Orazio Spina and Antonio J. Pe\~{n}a and Petter Sand\r{a}s and Iacopo Colonnelli and Alberto Scionti and Chiara Vercellino and Emanuele Dri and Jonathan Frassineti and Sara Marzella and Andrea Muratori and Daniele Ottaviani and Olivier Terzo and Bartolomeo Montrucchio and Daniele Gregori},
	doi = {10.1016/j.future.2026.108699},
	issn = {0167-739X},
	journal = {Future Generation Computer Systems},
	keywords = {streamflow, icsc},
	pages = {108699},
	title = {Three ways to share a QPU: Scheduling strategies for hybrid Quantum-HPC applications},
	url = {https://iris.unito.it/retrieve/e8086c96-8b84-47b7-bc9a-a43a34de633d/2604.14955v2.pdf},
	volume = {185},
	year = 2026,
	bdsk-url-1 = {https://iris.unito.it/retrieve/e8086c96-8b84-47b7-bc9a-a43a34de633d/2604.14955v2.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.future.2026.108699}}

@article{2026:sn:visivo,
	abstract = {Modern astrophysics and cosmology generate petabyte-scale datasets that demand advanced tools for scalable visualization and analysis. The Visualization Interface for the Virtual Observatory (VisIVO) provides multi-dimensional data exploration, but its integration with heterogeneous computing infrastructures and data-intensive workflows has remained challenging. Here we present a portable and reproducible approach that combines VisIVO with the StreamFlow workflow engine and the CAPIO middleware. StreamFlow enables hybrid execution across cloud-HPC environments, while CAPIO injects transparent I/O streaming into legacy file-based pipelines without modifying application code. Together, these technologies accelerate high--performance visualization, improve reproducibility, and reduce I/O bottlenecks. We demonstrate our approach on large-scale cosmological simulations produced with ChaNGa and GADGET-based codes, including studies of massive neutrinos in the DEMNUni suite. Our results show that streaming-enhanced workflows deliver up to 50{\%} performance gains and enable interactive, scalable visualization across distributed infrastructures, paving the way for exascale-ready scientific discovery.},
	author = {Sciacca, Eva and Tuccari, Nicola and Santimaria, Marco Edoardo and Cesare, Valentina and Vitello, Fabio and Mulone, Alberto and Medi{\'{c}}, Doriana and Colonnelli, Iacopo},
	doi = {10.1007/s42979-026-05076-4},
	issn = {2661-8907},
	journal = {SN Computer Science},
	keywords = {streamflow, space, icsc},
	month = jun,
	number = 5,
	pages = 527,
	title = {Hybrid Streaming Workflows for Scalable Visualization in Astronomy and Cosmology},
	url = {https://link.springer.com/content/pdf/10.1007/s42979-026-05076-4.pdf},
	volume = 7,
	year = {2026},
	bdsk-url-1 = {https://link.springer.com/content/pdf/10.1007/s42979-026-05076-4.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s42979-026-05076-4}}

@inproceedings{2026:icsuperhype,
	abstract = {Hypergraphs are graph generalizations with key applications in domains such as healthcare, where strict data privacy requirements apply, or bioinformatics, where testing new compounds is costly. However, due to their combinatorial nature, hypergraph representations are often either intractable or lead to significant information loss. For this reason, research into hypergraph synthesis is limited, and state-of-the-art approaches yield poor generation quality in terms of overall structural patterns and graph-level validity. To address such shortcomings, we introduce SuperHype, an exact and tractable hypergraph diffusion model. The core of SuperHype is the graph-superposition decomposition, a novel representation that embeds a hypergraph into a multi-layer graph, enabling a tractable representation with no loss of generalization. To generate new samples from such representations, we introduce a Graph-Superposition Transformer that treats the superposition as an interconnected sequence of layers. Moreover, we enhance the model's performance by incorporating hypergraph-specific auxiliary features and aggregating indirect node interactions via triplet pooling. Our evaluation across five datasets shows that SuperHype generally reproduces local and global connectivity patterns with superior fidelity compared to state-of-the-art baselines.},
	author = {Lucas Gantes and Abele M{\u{a}}lan and Roberto Gheda and Robert Birke and Lydia Chen},
	booktitle = {Forty-third International Conference on Machine Learning},
	keywords = {dyman, ai},
	title = {SuperHype: Hypergraph Generation via Graph-Superposition Decomposition},
	url = {https://openreview.net/forum?id=VasXWJMqSp},
	year = {2026},
	bdsk-url-1 = {https://openreview.net/forum?id=VasXWJMqSp}}

@article{Antelmi_OSNEM_2026,
	abstract = {Opinion dynamics models describe the evolution of behavioral changes within social networks and are essential for informing strategies aimed at fostering positive collective changes, such as climate action initiatives. When applied to social media interactions, these models typically represent social exchanges in a dyadic format to allow for a convenient encoding of interactions into a graph where edges represent the flow of information from one individual to another. However, this structural assumption fails to adequately reflect the nature of group discussions prevalent on many social media platforms. To address this limitation, we present a temporal hypergraph model that effectively captures the group dynamics inherent in conversational threads, and we apply it to discussions about climate change on Reddit. This model predicts temporal shifts in stance toward climate issues at the level of individual users. In contrast to traditional studies in opinion dynamics that typically rely on simulations or limited empirical validation, our approach is tested against a comprehensive ground truth estimated by a large language model at the level of individual user comments. Our findings demonstrate that using hypergraphs to model group interactions yields superior predictions of the microscopic dynamics of opinion formation, compared to state-of-the-art models based on dyadic interactions. Although our research contributes to the understanding of these complex social systems, significant challenges remain in capturing the nuances of how opinions are formed and evolve within online spaces.},
	author = {Alessia Antelmi and Carmine Spagnuolo and Luca Maria Aiello},
	doi = {10.1016/j.osnem.2026.100350},
	issn = {2468-6964},
	journal = {Online Social Networks and Media},
	keywords = {icsc, IT4LIA, analytics},
	pages = {100350},
	title = {Modeling the impact of group interactions on climate-related opinion change in Reddit},
	volume = {53},
	year = {2026},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S2468696426000066}}

@article{Antelmi_TOC_2026,
	abstract = {This study explores the structural properties of the Personality Inventory for the Diagnostic and Statistical Manual of Mental Disorders, fifth edition, Brief Form (PID-5-BF) by applying network analysis and community detection as a data-driven alternative to traditional factor models. Traditionally, the PID-5-BF assesses personality traits across five domains---Negative Affectivity, Detachment, Antagonism, Disinhibition, and Psychoticism---but has shown notable inconsistencies in item alignment and factorial coherence. To examine these issues, data were collected from 2,766 Italian participants (71.7% female, 28.3% male, Mage = 32.94 years, SD = 13.2). The estimated network revealed a stable structure, supported by robust centrality measures (closeness = 0.59, expected influence = 0.75, strength = 0.75). Community detection identified five empirically coherent clusters---Disinhibition, Demoralization, Detachment and Irritability, Psychosocial Alienation, and Pathological Egocentrism---suggesting an alternative organization of maladaptive traits in this population. To assess generalizability, a second analysis was conducted on a Hungarian sample (N = 355), yielding a five-structure solution with different item compositions. While the network approach emphasizes item-level associations, the specific configurations varied across samples in ways that reflect contextual influences. Nonetheless, this method offers complementary insights to traditional factorial models, highlighting how personality traits may organize differently across populations and supporting the use of network-based approaches in refining dimensional models of personality pathology.},
	author = {Oppici, L. and Antelmi, A. and Mazza, C. and Monaro, M. and Bosco, F. and Roma, P.},
	doi = {10.1037/per0000745},
	journal = {Personality Disorders: Theory, Research, and Treatment},
	keywords = {analytics},
	number = {1},
	pages = {30-41},
	title = {Mapping personality traits: A network approach to uncovering Personality Inventory for Diagnostic and Statistical Manual of Mental Disorders, fifth edition, Brief Form's factorial structure.},
	volume = {17},
	year = {2026},
	bdsk-url-1 = {https://psycnet.apa.org/record/2026-64942-001}}

@inproceedings{Antelmi_PDP_2026,
	abstract = {Understanding community formation is a fundamental task in network science, as it reveals the structural organization of complex networks and provides insights into the functional roles and interactions of their nodes. Recently, hypergraphs have emerged as a powerful framework for modeling high-order relationships in real-world systems, capturing multi-entity relations beyond traditional pairwise connections. However, efficiently processing hypergraphs on GPUs remains challenging due to their inherent sparsity and structural irregularity, leading to poor memory locality and load imbalance. Given that the world's most powerful supercomputers are equipped with GPUs from different vendors, such as AMD, Intel, and NVIDIA, a portable performance solution is essential to exploit these systems effectively without rewriting the entire codebase for each platform. In this work, we pursue this objective by evaluating three portable programming models, OpenMP, SYCL, and Kokkos, applied to the label propagation community detection algorithm for hypergraphs, examining their programmability and performance on heterogeneous GPU architectures.},
	author = {De Caro, Antonio and De Maio, Dario and Monzillo, Francesco and Antelmi, Alessia and Cosenza, Biagio},
	booktitle = {2026 34th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},
	doi = {10.23919/PDPCPS00030.2026.00015},
	keywords = {icsc, analytics, hpc},
	pages = {50-57},
	title = {Evaluating Portable Programming Models for Hypergraph Label Propagation on GPUs},
	year = {2026},
	bdsk-url-1 = {https://ieeexplore.ieee.org/abstract/document/11626255}}

@article{Antelmi_Scientometrics_2026,
	abstract = {In recent years, the rise of hyperprolific authors, researchers with exceptionally high publication counts, has drawn increasing attention in debates on research integrity and evaluation. While such productivity may reflect legitimate collaboration and large-scale science, it also raises concerns about questionable research practices, distorted incentives, and the reliability of scholarly assessment. Despite its prominence, the phenomenon remains poorly defined and inconsistently studied. This paper presents a systematic review of the literature on hyperprolific authorship to examine how it is defined, investigated, and perceived across disciplines. We identified 18 articles addressing hyperprolific authorship and 79 further contributions discussing related academic behaviors. Results show no consensus on thresholds or methodologies to identify hyperprolific authors, with approaches ranging from bibliometric analyses to qualitative assessments. The phenomenon is acknowledged across scientific domains, yet its interpretations diverge from being seen as a natural outcome of collaborative research to being framed as a statistical anomaly or a warning sign of misconduct. Its links to malpractice remain ambiguous, shaped by disciplinary norms, publication stages, and the intentionality of actors. By situating hyperprolific authorship within a broader ecosystem of academic (mis)behaviors, we conclude that it should not be reduced to a single category of misconduct but understood as a multifaceted, context-dependent phenomenon with important implications for research integrity and evaluation.},
	author = {Almeida, Jussara M. and Antelmi, Alessia and Gon{\c{c}}alves, Marcos Andr{\'e} and Pellegrino, Maria Angela},
	day = {02},
	doi = {10.1007/s11192-026-05563-8},
	issn = {1588-2861},
	journal = {Scientometrics},
	keywords = {icsc, analytics},
	month = mar,
	title = {The complex ecosystem of hyperprolific authors},
	url = {https://doi.org/10.1007/s11192-026-05563-8},
	year = {2026},
	bdsk-url-1 = {https://doi.org/10.1007/s11192-026-05563-8}}

@inproceedings{26:esann:fonio,
	abstract = {Prototype Learning offers an interpretable and efficient classification framework by mapping data into an embedding space structured around class prototypes. Recent research has explored non-Euclidean geometries, such as hyperspherical and hyperbolic spaces, to more effectively model latent hierarchical structures and complex data relationships. While these geometries have shown potential, leveraging them within an image classification context is not trivial. To address this, we propose HypPNet, a hyperbolic prototypical model on the Poincar´e ball that integrates Riemannian optimization and norm-based regularization to perform effectively without prior data knowledge. Experiments on three benchmark datasets and multiple embedding dimensions show that HypPNet outperforms its competitors across alternative geometries, improving classification performance over various metrics.},
	address = {Bruges, Belgium},
	author = {Grosso, Silvia and Fonio, Samuele and Polato, Mirko and Esposito, Roberto and Bouchenak, Sara},
	booktitle = {34th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, {ESANN}},
	keywords = {icsc, ai},
	month = apr,
	title = {When Curvature Counts: Hyperbolic Geometry in Prototype-Based Image Classification},
	url = {to appear},
	year = {2026},
	bdsk-url-1 = {to%20appear}}

@article{2026:textarossa:micpro,
	abstract = {The TEXTAROSSA project aims to bridge the technology gaps that exascale computing systems are currently facing and will be key in the near future to overcome performance and energy efficiency challenges. This project provides solutions for improved energy efficiency by using state-of-the-art cooling and thermal control, seamless integration of heterogeneous accelerators in HPC multi-node platforms, and new arithmetic methods tailored to heterogeneous hardware platforms. Challenges are tackled through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models, and tools derived from European research.},
	author = {Filgueras, Antonio and Agosta, Giovanni and Aldinucci, Marco and {\'A}lvarez, Carlos and D'Ambra, Pasqua and Bernaschi, Massimo and Biagioni, Andrea and Cattaneo, Daniele and Celestini, Alessandro and Celino, Massimo and Chiarini, Carlotta and Lo Cicero, Francesca and Cretaro, Paolo and Fornaciari, William and Frezza, Ottorino and Galimberti, Andrea and Giacomini, Francesco and de Haro Ruiz, Juan Miguel and Iannone, Francesco and Jaschke, Daniel and Jim{\'e}nez-Gonz{\'a}lez, Daniel and Kulczewski, Micha{\l} and Leva, Alberto and Lonardo, Alessandro and Martinelli, Michele and Martorell, Xavier and Montangero, Simone and Morais, Lucas and Oleksiak, Ariel and Palazzari, Paolo and Pontisso, Luca and Reghenzani, Federico and Rossi, Cristian and Saponara, Sergio and Lodi, Carlo Saverio and Simula, Francesco and Terraneo, Federico and Vicini, Piero and Vidal, Miquel and Zoni, Davide and Zummo, Giuseppe},
	doi = {10.1016/j.micpro.2026.105254},
	journal = {Microprocessors and Microsystems},
	keywords = {textarossa, icsc},
	title = {The TEXTAROSSA project: Cool all the Way Down to the Hardware},
	url = {https://www.sciencedirect.com/science/article/pii/S0141933126000116?pes=vor&utm_source=scopus&getft_integrator=scopus#sec3},
	volume = {122},
	year = {2026},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0141933126000116?pes=vor&utm_source=scopus&getft_integrator=scopus#sec3},
	bdsk-url-2 = {https://doi.org/10.1016/j.micpro.2026.105254}}

@article{2026:fcgs:performance,
	author = {Garcia, Adriano Marques and Malenza, Giulio and Birke, Robert and Aldinucci, Marco},
	doi = {10.1016/J.FUTURE.2025.108242},
	journal = {Future Gener. Comput. Syst.},
	keywords = {icsc, dare},
	pages = {108242},
	title = {Inference performance of large language models on a 64-core {RISC-V} {CPU} with silicon-enabled vectors},
	volume = {177},
	year = {2026},
	bdsk-url-1 = {https://doi.org/10.1016/J.FUTURE.2025.108242}}

@article{25:fgcs:ft,
	abstract = {In large-scale distributed systems, failures are routine events whose occurrences increase with the number of computational tasks and execution locations. The advantage of representing an application as a workflow is the possibility of exploiting Workflow Management System (WMS) features such as portability, scalability, and, crucially, reliability. Among these, reliability is essential for ensuring robust execution in dynamic and failure-prone environments. In recent years, the emergence of hybrid workflows has posed new and intriguing challenges by increasing the possibility of distributing computations involving heterogeneous and independent environments. Consequently, the number of possible points of failure during the execution increased, creating a need for sophisticated fault tolerance mechanisms capable of addressing the specific requirements of hybrid systems. This work introduces a formal framework for a fault tolerance mechanism in hybrid workflows, enabling failure recovery through a rollback approach. The framework is rigorously defined by adapting and extending an existing workflow semantics tailored for hybrid execution. Our method leverages provenance data from workflow execution up to the point of failure, and creates a recovery workflow that spans multiple infrastructures. The rollback approach provides a robust and reliable strategy to ensure resilience against step failures and potential data loss. We then implement this mechanism in the StreamFlow WMS, and evaluate it using two case studies: the 1000 Genomes workflow and a synthetic workflow featuring iterative patterns. Experiments showcase the conceptual validity of our approach and assess the overhead introduced by the mechanism, including data availability checks.},
	author = {Alberto Mulone and Doriana Medi\'{c} and Iacopo Colonnelli and Marco Aldinucci},
	doi = {10.1016/j.future.2025.108188},
	issn = {0167-739X},
	journal = {{F}uture {G}eneration {C}omputer {S}ystems},
	keywords = {icsc, eupex, space, streamflow},
	pages = {108188},
	title = {A formal framework for fault tolerance in hybrid scientific workflows},
	url = {https://iris.unito.it/retrieve/b9adc822-4455-4fe1-a8c1-8a66c084335e/1-s2.0-S0167739X25004820-main.pdf},
	volume = {176},
	year = {2026},
	bdsk-url-1 = {https://iris.unito.it/retrieve/b9adc822-4455-4fe1-a8c1-8a66c084335e/1-s2.0-S0167739X25004820-main.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.future.2025.108188}}

@article{25:FGCS:TEEs-Performance,
	abstract = {Data confidentiality is crucial when processing sensitive information, often limiting user interactions and shared computing services like the cloud. While Trusted Execution Environments (TEEs) offer a means to ensure privacy in untrusted environments, they frequently introduce significant computational overhead. DNA alignment, a key step in bioinformatics workflows, is privacy-sensitive and computationally intensive. Given its parallelizable nature, it is a compelling case study for evaluating the performance impact and scalability of various TEEs. This study assesses three TEEs -- Intel SGX, Intel TDX, and AMD SEV-SNP -- by evaluating their overhead through real-world bioinformatics workloads and system-level microbenchmarks. Our evaluation shows that SGX-based solutions incur substantial overhead, particularly for small workloads, with slowdowns ranging from 283% to 1971% compared to native execution. The overhead is reduced for larger workloads, ranging from 15% to 57%. In contrast, TDX and SEV-SNP offer significantly improved performance: TDX limits overhead to 73% for small and to 9% for large workloads, while SEV-SNP incurs at most 67% and 29%, respectively. Importantly, SEV-SNP demonstrates better scalability than TDX, a result supported by microbenchmark analysis showing more efficient thread creation and scheduling. Conversely, TDX shows more efficient memory utilization, underscoring distinct overhead sources among the evaluated TEE architectures.},
	author = {Lorenzo Brescia and Iacopo Colonnelli and Robert Birke and Valerio Schiavoni and Pascal Felber and Marco Aldinucci},
	doi = {10.1016/j.future.2025.108031},
	issn = {0167-739X},
	journal = {Future Generation Computer Systems},
	keywords = {icsc, confidential},
	pages = {108031},
	title = {A comprehensive performance evaluation of TEEs for confidential DNA alignment},
	url = {https://www.sciencedirect.com/science/article/pii/S0167739X25003267},
	year = {2026},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0167739X25003267},
	bdsk-url-2 = {https://doi.org/10.1016/j.future.2025.108031}}

@article{26:FGCS:Terminology,
	abstract = {The term ``scientific workflow'' has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow systems have been developed to manage and run these scientific workflows. However, no turnkey solution has emerged from the field to address the diversity of scientific processes and the infrastructure on which they are supposed to be implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new workflow system. A direct consequence of this situation is that many existing workflow management systems (WMSs) share some salient features, offer similar functionalities, and can manage the same categories of workflows but at the same time also have some distinct capabilities that can be important for specific applications. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the computing and storage resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. This terminology is composed of fives axes: workflow structure and characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.},
	author = {Suter, Fr\'{e}d\'{e}ric and Coleman, Tain\~{a} and Altinta\c{s}, \.{I}lkay and Badia, Rosa M. and Balis, Bartosz and Chard, Kyle and Colonnelli, Iacopo and Deelman, Ewa and Di Tommaso, Paolo and Fahringer, Thomas and Goble, Carole and Jha, Shantenu and Katz, Daniel S. and K"{o}ster, Johannes and Leser, Ulf and Mehta, Kshitij and Oliver, Hilary and Peterson, J.-Luc and Pizzi, Giovanni and Pottier, Lo"{i}c and Sirvent, Ra"{u}l and Suchyta, Eric and Thain, Douglas and R. Wilkinson, Sean and Wozniak, Justin M. and Ferreira da Silva, Rafael},
	doi = {10.1016/j.future.2025.107974},
	issn = {0167-739X},
	journal = {Future Generation Computer Systems},
	keywords = {streamflow},
	pages = {107974},
	title = {A terminology for scientific workflow systems},
	url = {https://www.arxiv.org/abs/2506.07838},
	volume = {174},
	year = {2026},
	bdsk-url-1 = {https://www.arxiv.org/abs/2506.07838},
	bdsk-url-2 = {https://doi.org/10.1016/j.future.2025.107974}}

@article{25:fgcs:capio,
	abstract = {Advances in big data and the growth in complexity of modern applications highlight the necessity for optimizing workflow executions on different levels, such as hybrid workflow executions, automatic optimization of data movements, and efficient use of IO. Following this line, streaming features are the desired capabilities for file-based workflows as they can reduce overall execution times. Expanding workflows with streaming capabilities usually requires rewriting the application, which is time-consuming and requires deep knowledge of the application. With this work, we introduce the Cross-Application Programmable IO (CAPIO) methodology, of which the stack is composed of two parts: the CAPIO-CL coordination language and the CAPIO middleware (which implements the semantics expressed by the CAPIO-CL coordination language). The CAPIO-CL coordination language annotates synchronization semantics between files produced and consumed by workflow steps. At the same time, the CAPIO middleware improves the performance of file-based workflows, leveraging the information provided by the CAPIO-CL language while not having to change (recompile) the code of the original workflow steps. By design, the CAPIO middleware supports multiple backends and can be extended to support more. It is dynamic, and it supports dynamic job scheduling. Benchmarks, done on both microbenchmarks and real-life workflows, prove that with CAPIO, it is possible to reduce the workflow execution time by up to 50%},
	author = {Santimaria, Marco Edoardo and Colonnelli, Iacopo and Cantalupo, Barbara and Torquati, Massimo and Medi\'{c}, Doriana and Tuccari, Nicola and Sciacca, Eva and Aldinucci, Marco},
	date-modified = {2026-08-14 12:42:32 +0200},
	doi = {10.1016/j.future.2025.108159},
	issn = {0167-739X},
	journal = {Future Generation Computer Systems},
	keywords = {capio, eupex, space, icsc},
	month = {March},
	pages = {108159},
	publisher = {Elsevier BV},
	title = {Dynamic transparent streaming in file-based workflows with CAPIO},
	url = {http://dx.doi.org/10.1016/j.future.2025.108159},
	year = {2026},
	bdsk-url-1 = {http://dx.doi.org/10.1016/j.future.2025.108159}}

@inproceedings{DBLP:conf/europar/BifulcoMMRA26,
  author       = {Mario Bifulco and
                  Francesco Medina and
                  Doriana Medic and
                  Luca Roversi and
                  Marco Aldinucci},
  editor       = {Massimo Torquati and
                  Gabriele Mencagli and
                  Valeria Cardellini and
                  Alessia Antelmi and
                  Doriana Medic},
  title        = {QSplit: {A} Workflow-Oriented Hybrid Quantum-Classical Optimization
                  Framework},
  booktitle    = {Euro-Par 2026: Parallel Processing - 32nd European Conference on Parallel
                  and Distributed Processing, Pisa, Italy, August 24-28, 2026, Proceedings,
                  Part {II}},
  series       = {Lecture Notes in Computer Science},
  volume       = {16782},
  pages        = {195--209},
  publisher    = {Springer},
  year         = {2026},
  url          = {https://doi.org/10.1007/978-3-032-35251-4\_14},
  doi          = {10.1007/978-3-032-35251-4\_14},
  timestamp    = {Tue, 01 Sep 2026 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/europar/BifulcoMMRA26.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:journals/corr/abs-2501-05259,
  author       = {Matteo Palazzo and
                  Luca Roversi},
  editor       = {Thorsten Altenkirch and
                  Franco Barbanera and
                  Ferruccio Damiani and
                  Ugo de'Liguoro},
  title        = {Reversible Computation with Stacks and "Reversible Management of Failures"},
  booktitle    = {Logics and Type Theory: essays dedicated to Stefano Berardi on the
                  occasion of his 1000000th birthday, Logics and Type Theory 2026, Turin,
                  Italy, 12th April 2026},
  series       = {{EPTCS}},
  volume       = {441},
  pages        = {213--226},
  year         = {2026},
  month        = jan,
  url          = {https://doi.org/10.4204/EPTCS.441.13},
  doi          = {10.4204/EPTCS.441.13},
  timestamp    = {Mon, 03 Aug 2026 15:52:43 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2501-05259.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/rc/2026,
  editor       = {Cl{\'{e}}ment Aubert and
                  Luca Roversi},
  title        = {Reversible Computation - 18th International Conference, {RC} 2026,
                  Turin, Italy, July 9-10, 2026, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {16626},
  publisher    = {Springer},
  year         = {2026},
  url          = {https://doi.org/10.1007/978-3-032-30839-9},
  doi          = {10.1007/978-3-032-30839-9},
  isbn         = {978-3-032-30838-2},
  timestamp    = {Thu, 23 Jul 2026 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/rc/2026.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/fgcs/AntelmiC26,
  author       = {Alessia Antelmi and
                  Emanuele Carlini},
  title        = {Large-scale {HPC} approaches and applications on highly distributed
                  platforms},
  journal      = {Future Gener. Comput. Syst.},
  volume       = {179},
  pages        = {108365},
  year         = {2026},
  url          = {https://doi.org/10.1016/j.future.2025.108365},
  doi          = {10.1016/J.FUTURE.2025.108365},
  timestamp    = {Wed, 25 Feb 2026 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/fgcs/AntelmiC26.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{Santimaria2026-TMT-hipes-25,
  title = {Thread Monitoring Tool: Transparent Characterization of Threading Patterns with eBPF},
  isbn = {9783032191441},
  issn = {1611-3349},
  url = {http://dx.doi.org/10.1007/978-3-032-19144-1_20},
  doi = {10.1007/978-3-032-19144-1_20},
  booktitle = {Lecture Notes in Computer Science},
  publisher = {Springer Nature Switzerland},
  author = {Santimaria,  Marco Edoardo and Marques Garcia,  Adriano and Malenza,  Giulio and Monaldi,  Stefano and Aldinucci,  Marco and Birke,  Robert},
  year = {2026},
  keywords = {icsc,dare},
  pages = {253–264},
  journal = {Lecture Notes in Computer Science},
  abstract = {Controlling the number of threads in parallel computation is a key tuning factor for performance. If too few, the program does not leverage all the available hardware resources. If too many, thread management overhead and bottleneck-resource congestion slow down execution. A good rule-of-thumb starting value is often one software thread for each hardware core. However, complex software may use multiple parallelism at different (nested) levels, making it difficult to predict what is created during execution. Moreover, classic monitoring tools only sample the process tree at given time instants, which misses any thread not yet created or already destroyed before sampling happens. Here, we present a thread monitoring tool that traces kernel events for continuous tracking of threads spawned and joined by a program during its execution with little to no overhead. Evaluation of simple applications running with different parallel backends, as well as PyTorch with the LAMA-3.2-1B and Bzip2 running with two different parallel backends, shows the tool’s capabilities.},
}

@inproceedings{Antelmi_CIKM_2026,
	author = {Antelmi, Alessia and Citro, Tiziano and De Maio, Dario and De Vinco, Daniele and Di Pasquale, Valerio and Polato, Mirko and Spagnuolo, Carmine},
	title = {Hyperlink Prediction on Hypergraphs of Text},
	year = {2026},
	isbn = {9798400725395},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	bdsk-url-1 = {https://doi.org/10.1145/3799682.3839872},
	doi = {10.1145/3799682.3839872},
	abstract = {Hypergraphs are powerful tools for modeling high-order relationships in complex real-world data, and hyperlink prediction has emerged from the need to infer relationships that are missing or have not yet formed. Existing hyperlink prediction methods primarily emphasize the structural connectivity encoded in the network, overlooking the rich semantic information associated with nodes and hyperedges that can be expressed in text. In this work, we propose a novel framework for hyperlink prediction on Hypergraphs of Text (HoTs), where both nodes and hyperedges are enriched with textual attributes. Our model jointly leverages these semantic and structural signals by combining hypergraph convolutional operators with cross-attention mechanisms that iteratively refine node and hyperedge representations. Experimental results demonstrate that integrating semantic information from nodes and hyperedges with structural features consistently improves performance over baselines that rely solely on topology, thereby highlighting the effectiveness of contextual representations for hyperlink prediction and opening new directions for semantic-aware hypergraph learning.},
	booktitle = {Proceedings of the 35th ACM International Conference on Information and Knowledge Management},
	pages = {6872–6876},
	numpages = {5},
	keywords = {IT4LIA, analytics, ai},
	location = {Rome, Italy},
	series = {CIKM '26}
}

@inproceedings{bridda2026t2f,
  author    = {Consuelo Bridda and Doriana Medi{\'c} and Lavinia Chiara Tagliabue and Robert Birke and Marco Aldinucci},
  title     = {T2F: Forecasting Irregularly Sampled Time Series via Time Features},
  booktitle = {Accepted for publication in: 2026 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS)},
  year      = {2026},
  address   = {Brescia, Italy},
  keywords  = {ai}
}

@article{casella2026minifed2rc,
	title        = {Fast Federated Time Series Classification using Rocket Kernels and Closed-form Classifiers},
	author       = {Casella, Bruno and Fonio, Samuele and Sciandra, Lorenzo and Aldinucci, Marco and Gallicchio, Claudio and Polato, Mirko and Esposito, Roberto},
	year         = 2026,
	journal      = {Data Mining and Knowledge Discovery},
	%%volume       = {TODO},
	%%pages        = {TODO--TODO},
	%%doi          = {TODO},
	%%url          = {TODO},
	abstract     = {Fed2RC is a fully federated, gradient-free method for Time Series Classification (TSC) that leverages Rocket kernels and the exact aggregation of ridge regression statistics through the closed-form solution. We propose two extended versions of Fed2RC: Fed2RC+ and MiniFed2RC. Fed2RC+ is more accurate than Fed2RC in the multiclass scenario as it enhances the feature selection step via a confidence score for each one-versus-rest classifier. MiniFed2RC employs MiniRocket, a fast and nearly deterministic version of Rocket, for feature extraction by averaging the biases, the sole source of randomness. Extensive experiments on the UCR benchmark demonstrate that Fed2RC+ and MiniFed2RC achieve state-of-the-art results, under both i.i.d. and non-i.i.d. distributions, cross-silo and cross-device scenarios, and with low computation and communication costs. Code to reproduce the experiments can be found at: https://github.com/CasellaJr/Fed2RC.},
	keywords     = {ai, fl, icsc, epi, eupilot},
	%%bdsk-url-1   = {TODO},
}

@inproceedings{casella2026multivarfed2rc,
	title        = {Experimenting with Federated Learning for Multivariate Time Series Classification},
	author       = {Casella, Bruno and Sciandra, Lorenzo},
	year         = 2026,
	optmonth	 = {11},
	booktitle    = {The 5th Italian Conference on Big Data and Data Science - ITADATA2026},
	pages        = {1--12},
	%%doi          = {TODO},
	%%url          = {TODO},
	abstract 	 = {Time Series Classification (TSC) is a pivotal task in modern machine learning, with widespread applications in sensitive domains such as healthcare, finance, and cybersecurity. In real-world scenarios, data are inherently distributed and cannot be centralized due to strict privacy regulations, making federated learning a natural and necessary paradigm for collaborative model training. While several TSC methods have achieved strong performance in centralized settings, their gradient-based nature often results in prohibitive communication costs and limited efficiency when deployed in federated environments. In this context, MiniFed2RC has recently emerged as a fully federated, gradient-free approach for univariate TSC, combining random Rocket kernels with a closed-form ridge regression classifier. It has demonstrated state-of-the-art accuracy on the UCR univariate benchmark while significantly reducing computation and communication overhead. However, its effectiveness on multivariate time series—which are increasingly common in practice—has not yet been explored. In this paper, we present the first comprehensive benchmark of MiniFed2RC on the UEA multivariate time series classification archive. We evaluate the method across all 30 datasets, analyzing its predictive performance and communication costs. Our results show that MiniFed2RC achieves competitive classification performance on multivariate data, maintaining the same efficiency and robustness observed in the univariate setting. Code is available at https://github.com/CasellaJr/Fed2RC.},
	keywords	 = {ai, epi, icsc, eupilot},
}

@inproceedings{26:mittone:fldp,
	title        = {Accelerating Sharded Data Parallelism at Scale with Federated Learning},
	author       = {Mittone, Gianluca and Aldinucci, Marco},
	year         = 2026,
	month        = {08},
	booktitle    = {Euro-Par 2026: Parallel Processing - 32nd European Conference on Parallel and Distributed Processing, Pisa, Italy, August 24-28, 2026, Proceedings, Part {II}},
	address      = {Pisa, Italy},
	url          = {https://doi.org/10.1007/978-3-032-35251-4\_30},
	note         = {https://arxiv.org/abs/2609.20359},
	abstract     = {The symbiotic scaling of artificial intelligence models and high-performance computing systems continually creates algorithmic challenges in their convergence. Foundation models (FMs) are a crucial example, requiring months-long training on thousands of cutting-edge GPUs. Sharded data parallelism (DP) is the dominant strategy to accelerate such computations by splitting data and models across multiple GPUs. However, it incurs prohibitive communication overhead when deployed at scale, particularly on multi-tier interconnects with heterogeneous performance. Inspired by the efficient communication principles of federated learning (FL), this work introduces two hybrid algorithms - FL+FSDP and FL+HSDP - interleaving sharded DP with FedAvg-style aggregations. Such approaches decouple large DP deployments into smaller, loosely-coupled federation groups, requiring minimal inter-group traffic while keeping the global batch size bounded by the groups' size. Formal analysis of communication costs and experimental validation prove their scalability and flexibility. A Llama3.1 8B pre-training on 512 A100 GPUs shows that, under identical hyperparameters, FL+FSDP and FL+HSDP achieve up to 8.04 faster data processing and 4.48 lower evaluation perplexity than their counterparts, demonstrating superior computational efficiency and improved model quality. These properties stem from reduced communication overhead and the bounded growth of the global batch size relative to the federation group size.},
	keywords     = {ai, fl, hpc, dyman, icsc, space}
}

@inproceedings{Grandi2026Roc2Img,
	title        = {A Lightweight Random Feature Framework for Efficient Image Classification},
	author       = {Grandi, Luca and Signoretti, Loris and Sciandra, Lorenzo and Casella, Bruno and Aldinucci, Marco and Esposito, Roberto},
	year         = 2026,
	month 		 = {09},
	booktitle    = {35th International Conference on Artificial Neural Networks – ICANN 2026},
	address		 = {Padova, Italy},
	pages        = {1--12},
	doi          = {10.1007/978-3-032-38401-0_54},
	url          = {https://iris.unito.it/retrieve/e5f10488-0568-4d6c-ada7-692b710240db/_ICANN_26__Roc2Imgs.pdf},
	keywords	 = {ai, epi, icsc, eupilot},
}

@inproceedings{2026:iwsg:fogliobonda,
	title        = {Towards User-Friendly {HPC}: A Web Platform for Hybrid Workflows},
	author       = {Foglio Bonda, Tommaso and Lusso, Ivan and Medi{\'c}, Doriana and Antelmi, Alessia and Sciacca, Eva and Tuccari, Nicola and Vitello, Fabio},
	year         = 2026,
	month        = jun,
	booktitle    = {Proceedings of the 18th International Workshop on Science Gateways (IWSG 2026)},
	address      = {Catania, Italy},
	keywords     = {streamflow, hpc4ai, space, visivo}
}

@inproceedings{2026:csr:bagnato,
	title        = {A Privacy-Preserving Hybrid Machine Learning Pipeline for Regulated Environments: Architecture and Evaluation},
	author       = {Bagnato, Roberto and Medi{\'c}, Doriana and Vollero, Luca and Sabatini, Anna and Foglio Bonda, Tommaso and Innocenti, Manuele and Aldinucci, Marco},
	year         = 2026,
	month        = aug,
	booktitle    = {2026 IEEE International Conference on Cyber Security and Resilience (CSR) Workshops (2P-DPA)},
	publisher    = {IEEE},
	address      = {Lisbon, Portugal},
	keywords     = {streamflow, hpc4ai, confidential}
}

@inproceedings{cajas2025embeddingqsvm,
  author    = {Sebasti{\'a}n Andr{\'e}s Cajas Ord{\'o}{\~n}ez and
               Luis Fernando Torres Torres and
               Mario Bifulco and
               Carlos Andres Duran and
               Cristian Bosch and
               Ricardo Simon Carbajo},
  title     = {Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning},
  booktitle = {Proceedings of the 3rd International Workshop on AI for Quantum and Quantum for AI (AIQxQIA 2025)},
  series    = {CEUR Workshop Proceedings},
  volume    = {4153},
  year      = {2025},
  publisher = {CEUR-WS.org},
  url       = {https://ceur-ws.org/Vol-4153/paper21.pdf},
  issn      = {1613-0073}
}

@article{Malenza2025OpenFOAMCpp,
  author        = {Malenza, Giulio and Stabile, Giovanni and Spiga, Filippo and Birke, Robert and Aldinucci, Marco},
  title         = {Building an Accelerated {OpenFOAM} Proof-of-Concept Application using Modern {C++}},
  journal       = {arXiv preprint arXiv:2507.18268},
  year          = {2025},
  eprint        = {2507.18268},
  archiveprefix = {arXiv},
  primaryclass  = {cs.DC},
  doi           = {10.48550/arXiv.2507.18268},
  url           = {https://arxiv.org/abs/2507.18268}
}

@article{25:CSCW:Antelmi,
	abstract = {The huge and ever-increasing amount of publicly available data is shaping the data-driven society that citizens are encouraged to tame. It requires future generations, i.e., current learners, to acquire data literacy skills to make informed decisions. Towards this direction, we present a data literacy workshop involving more than {\$}{\$}{\backslash}varvec{\{}150{\}}{\$}{\$}high school learners focused on co-creating Open Data via a digital environment and authoring data stories while evaluating their engagement and learning. Results show that participants are, on average, engaged during the in-person stages of the workshops, independently by gender, and they are mainly interested in collaborative activities, hands-on, and public presentations. This experiment confirm that learning is positively correlated with engagement, which aligns with the literature. However, further efforts should be invested in letting learners master data literacy skills while increasing their interest.},
	author = {Pellegrino, Maria Angela and Antelmi, Alessia and Spagnuolo, Carmine and Scarano, Vittorio},
	day = {01},
	doi = {10.1007/s10606-025-09529-x},
	issn = {1573-7551},
	journal = {Computer Supported Cooperative Work (CSCW)},
	keywords = {icsc, analytics, opendata},
	month = dec,
	number = {4},
	pages = {1079--1120},
	title = {Open Data in Education: Fostering Data Literacy Among High-school Learners},
	url = {https://doi.org/10.1007/s10606-025-09529-x},
	volume = {34},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1007/s10606-025-09529-x}}

@inproceedings{2025:dsd:dare,
	abstract = {The European Union's efforts towards technological sovereignty in High-Performance Computing are driving research and development of RISC-V-based supercomputers. The DARE SGA1 project, in particular, aims to develop chips designed and owned by Europeans. This paper introduces the Italian contribution to DARE SGA1 regarding pathfinding activities toward future RISC-V-based accelerator designs, reliability improvements, system software, and AI and Quantum Chemistry applications.},
	author = {Agosta, Giovanni and Aldinucci, Marco and Bartolini, Andrea and Bellentani, Laura and Biagioni, Andrea and Cesarini, Daniele and Chiarini, Carlotta and Colonnelli, Iacopo and Delugas, Pietro and Denisov, Lev and Frezza, Ottorino and Grangetto, Marco and Lo Cicero, Francesca and Lonardo, Alessandro and Martinelli, Michele and Maslov, Andrea and Olivieri, Mauro and Perticaroli, Pierpaolo and Pontisso, Luca and Rossi, Cristian and Rossi, Davide and Saponara, Sergio and Sciarappa, Antonio and Simula, Francesco and Sonza Reorda, Matteo and Torquati, Massimo and Vicini, Piero},
	booktitle = {28th Euromicro Conference on Digital System Design, {DSD} 2025},
	doi = {10.1109/DSD67783.2025.00057},
	keywords = {dare, streamflow, capio},
	location = {Salerno, Italy},
	mon = {sep},
	pages = {360--367},
	publisher = {{IEEE}},
	title = {Towards {RISC-V}-based {HPC:} The Italian Pathfinding Activities in the {DARE-SGA1} Project},
	url = {https://iris.unito.it/retrieve/03658af6-782d-4f9c-9108-b0fc1f1859eb/DARE_ICSC_EPDSD2025-1.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/03658af6-782d-4f9c-9108-b0fc1f1859eb/DARE_ICSC_EPDSD2025-1.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/DSD67783.2025.00057}}

@inproceedings{2025:qce:hybrid,
	abstract = {The integration of quantum computers within classical High-Performance Computing (HPC) infrastructures is receiving increasing attention, with the former expected to serve as accelerators for specific computational tasks. However, combining HPC and quantum computers presents significant technical challenges, including resource allocation. This paper presents a novel malleability-based approach, alongside a workflow-based strategy, to optimize resource utilization in hybrid HPC-quantum workloads. With both these approaches, we can release classical resources when computations are offloaded to the quantum computer and reallocate them once quantum processing is complete. Our experiments with a hybrid HPC-quantum use case show the benefits of dynamic allocation, highlighting the potential of those solutions.},
	author = {Rocco, Roberto and Rizzo, Simone and Barbieri, Matteo and Bettonte, Gabriella and Boella, Elisabetta and Ganz, Fulvio and Iserte, Sergio and Pe{\~{n}}a, Antonio J. and Sand{\aa}s, Petter and Scionti, Alberto and Terzo, Olivier and Vercellino, Chiara and Vitali, Giacomo and Viviani, Paolo and Frassineti, Jonathan and Marzella, Sara and Ottaviani, Daniele and Colonnelli, Iacopo and Gregori, Daniele},
	booktitle = {2025 {IEEE} International Conference on Quantum Computing and Engineering (QCE)},
	doi = {10.1109/QCE65121.2025.10289},
	keywords = {streamflow, icsc},
	location = {Albuquerque, NM, USA},
	month = sep,
	pages = {34--40},
	publisher = {{IEEE}},
	title = {Dynamic Solutions for Hybrid Quantum-HPC Resource Allocation},
	url = {https://iris.unito.it/retrieve/47e89e49-136a-48ff-beeb-63cee8bdceab/Preprint.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/47e89e49-136a-48ff-beeb-63cee8bdceab/Preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/QCE65121.2025.10289}}

@inproceedings{gheda2025collaborative,
	abstract = {Bayesian Network models are a powerful tool to collaboratively optimize production processes in various manufacturing industries. When interacting, collaborating parties must preserve their business secrets by maintaining the confidentiality of their model structures and parameters. While most realistic industry scenarios involve hybrid settings, handling both discrete and continuous data, current state-ofthe-art methods for collaborative and confidential inference only support discrete data and have high communication costs. In a centralized setting, Junction Trees enable efficient inference even in hybrid scenarios without discretizing continuous variables, but no extension for collaborative and confidential scenarios exists. To address this research gap, we introduce Hybrid CCJT, the first framework for confidential multiparty inference in hybrid domains with semi-honest, non-colluding adversaries, comprising: (i) a method to construct a strongly-rooted Junction Tree across collaborating parties through a novel construct of interface cliques; and, (ii) a protocol for confidential inference built upon multiparty computation primitives comprising a one-time alignment phase and a belief propagation system for combining the inference results across the Junction Tree cliques. Extensive evaluation on nine datasets shows that Hybrid CCJT improves the predictive accuracy of continuous target variables by 32% on average compared to the state-of-the-art, while reducing communication costs by a median 10.4× under purely discrete scenarios.},
	author = {Gheda, Roberto and M{\u{a}}lan, Abele and Guzella, Thiago and Lancia, Carlo and Birke, Robert and Chen, Lydia Y.},
	booktitle = {Proceedings of The Thirty-ninth Annual Conference on Neural Information Processing Systems},
	keywords = {dyman, icsc, ai},
	location = {San Diego, CA, USA},
	title = {Collaborative and Confidential Junction Trees for Hybrid Bayesian Networks},
	url = {https://openreview.net/forum?id=ehfIVCHxkX},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/04313a93-09f6-4bb3-98e8-d2e8225cf7f3/22683_Collaborative_and_Confid.pdf}}

@inproceedings{fonio2025perfed,
	abstract = {Federated Learning (FL) systems face numerous challenges, including data heterogeneity, communication constraints, and vulnerability to adversarial attacks. A promising approach that has recently gained attention is prototype learning, which gives the possibility to share only local prototypes rather than entire models. However, the reliability of such systems is often overlooked, leading to insufficient attention to potential attack vectors. In this paper, we investigate the robustness of two Federated Prototypical methods under both random and revert poisoning attacks. We propose an extension of these methods using personalized aggregation in two different ways to enhance their resilience. Our approach provides valuable insights into the reliability of Federated Prototype Learning and contributes to the development of more robust FL systems. We validate our findings through an extensive experimental evaluation on Cifar-10 and EMNIST, with gains in performance up to 10% and 20%, respectively, with respect to the baselines.},
	author = {Fonio, Samuele and Casella, Bruno and Aldinucci, Marco},
	booktitle = {Proceedings of the ECML-PKDD Workshop, 3nd workshop on advancements in Federated Learning},
	keywords = {epi, icsc},
	location = {Porto, Portugal},
	title = {{Personalized Aggregation for Federated Prototypical Learning}},
	url = {https://iris.unito.it/retrieve/47a8316b-62b7-4142-a0cc-a867d37175e3/wafl_preprint.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/47a8316b-62b7-4142-a0cc-a867d37175e3/wafl_preprint.pdf}}

@inproceedings{casella2025drocks,
	abstract = {Time series classification (TSC) is a critical task with applications in various domains, including healthcare, finance, and industrial monitoring. Due to privacy concerns and data regulations, Federated Learning has emerged as a promising approach for learning from distributed time series data without centralizing raw information. However, most FL solutions rely on a client-server architecture, which introduces robustness and confidentiality risks related to the distinguished role of the server, which is a single point of failure and can observe knowledge extracted from clients. To address these challenges, we propose DROCKS, a fully decentralized FL framework for TSC that leverages ROCKET (RandOm Convolutional KErnel Transform) features. In DROCKS, the global model is trained by sequentially traversing a structured path across federation nodes, where each node refines the model and selects the most effective local kernels before passing them to the successor. Extensive experiments on the UCR archive demonstrate that DROCKS outperforms state-of-the-art client-server FL approaches while being more resilient to node failures and malicious attacks. Our code is available at https://anonymous.4open.science/r/DROCKS-7FF3/README.md.},
	author = {Casella, Bruno and Jakobs, Matthias and Aldinucci, Marco and Buschjaeger, Sebastian},
	booktitle = {Proceedings of the ECML-PKDD Workshop, 3nd workshop on advancements in Federated Learning},
	keywords = {epi, icsc},
	location = {Porto, Portugal},
	title = {{Decentralized Time Series Classification with ROCKET Features}},
	url = {https://iris.unito.it/retrieve/b3d4f7f0-c7ec-4ad3-96be-6b55add95b6a/FAIA-413-FAIA251198.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/b3d4f7f0-c7ec-4ad3-96be-6b55add95b6a/FAIA-413-FAIA251198.pdf}}

@inproceedings{Santimaria:works:25,
	abstract = {This work introduces a novel double-sided streaming methodology that combines control-plane and data-plane streaming. Our goal is to implement the long-advocated separation of concerns in workflow orchestration without introducing artificial boundaries in their execution. Our approach is exemplified by the integration of control-plane streaming provided by dispel4py and the transparent data-plane streaming provided by CAPIO. Our integration eliminates file synchronization barriers without requiring modifications to existing workflow logic. To support this, we extend CAPIO with a new commit rule that allows streaming over dynamically generated file sets, enabling hybrid workflows that blend in-memory dataflows with file-based communication. We validate our approach using a real-world seismic cross-correlation workflow, achieving performance improvements between 23% and 40%. Unlike previous solutions, our method supports streaming across the entire workflow, including phase boundaries where file I/O would typically enforce strict execution ordering. Therefore, our approach can be straightforwardly extended to other multi-stage streaming applications.},
	author = {Santimaria, Marco Edoardo and Filgueira, Rosa and Medi\'{c}, Doriana and Colonnelli, Iacopo and Aldinucci, Marco},
	booktitle = {Proceedings of the SC 25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis},
	collection = {SC Workshops - 25},
	doi = {10.1145/3731599.3767577},
	keywords = {capio,dare,icsc},
	month = nov,
	pages = {2269--2280},
	publisher = {ACM},
	series = {SC Workshops 25},
	title = {Overcoming Dynamic I/O Boundaries: a Double-Sided Streaming Methodology with dispel4py and CAPIO},
	url = {http://dx.doi.org/10.1145/3731599.3767577},
	year = {2025},
	bdsk-url-1 = {http://dx.doi.org/10.1145/3731599.3767577}}

@inproceedings{Pernice:2025,
	author = {Pernice, Simone and Tarraf, Ahmad and Besnard, Jean-Baptiste and Cantalupo, Barbara and Cascajo, Alberto and Singh, David E. and Wolf, Felix and Carretero, Jes{\'u}s and Shende, Sameer and Aldinucci, Marco},
	booktitle = {2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	doi = {10.1109/IPDPSW66978.2025.00204},
	keywords = {admire},
	note = {Poster at IPDPS},
	pages = {1258--1260},
	title = {A Simulation-Based Framework to Reduce I/O Contention in HPC},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1109/IPDPSW66978.2025.00204}}

@article{admire:2025,
	abstract = {Due to new workloads arising with workflows and AI applications, dynamic resource allocation can improve productivity across all system and application levels by adapting the applications' configurations to the system's resources. However, HPC system software is not suited nowadays to provide dynamic resource management (DRM) for computing or I/O resources in runtime. This paper presents the ADMIRE framework and its mechanisms for dynamic resource management. ADMIRE's efforts enable an integrated stack in which storage, compute, and orchestration layers were co-designed and validated across a shared, heterogeneous testbed.},
	author = {Carretero, Jesus and Singh, David E. and Fernandez-Mu{\~n}oz, Javier and Sedona, Rocco and Pernice, Simone and Cantalupo, Barbara and Aldinucci, Marco and Torquati, Massimo and Tarraf, Ahmad and Jeannot, Emmanuel and Wolf, Felix},
	booktitle = {Proceedings of the Third EuroHPC user day},
	doi = {https://doi.org/10.1016/j.procs.2025.08.243},
	issn = {1877-0509},
	journal = {{P}rocedia {C}omputer {S}cience},
	keywords = {admire},
	pages = {165--175},
	title = {Dynamic Resources Management for Exascale in ADMIRE Framework},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050925025852},
	volume = {267},
	year = {2025},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S1877050925025852},
	bdsk-url-2 = {https://doi.org/10.1016/j.procs.2025.08.243}}

@inproceedings{DBLP:conf/eScience/ContaldoBRPRABC25,
	abstract = {Modern scientific discovery is frequently backed by largescale scientific experiments, which cannot prescind from the unparalleled computational capabilities provided by highperformance computing systems. However, large data centers usually prioritize system efficiency over user accessibility, posing challenges for researchers without advanced computer science expertise. This work introduces BookedSlurm, a secure and userfocused extension of the Slurm workload manager, aiming to democratize HPC access across interdisciplinary research domains. BookedSlurm enables a partially decentralized regulation of finegrained advanced resource reservations through a novel creditbased framework, ensuring fair and predictable access to computing resources. Its modular architecture leverages dedicated microservices to manage reservations, credit handling, and accounting. These components are exposed through a secure REST API and an intuitive webbased dashboard, enhancing system usability for novice and expert users. While the dashboard simplifies interactions for nonspecialists, advanced users can directly access agentlevel APIs for more complex and automated operations. The effectiveness of BookedSlurm is validated on a realworld bioinformatics use case from the SUSMIRRI.IT project, showcasing its ability to enhance usability, optimize job scheduling, and streamline execution workflows.},
	address = {Los Alamitos, CA, USA},
	author = {Gepiro Contaldo, Sandro and Bosio, Lorenzo and Hoyos Rea, Janneth Estefania and Perottino, Elisa Li and Rabellino, Sergio and Aldinucci, Marco and Beccuti, Marco and Colonnelli, Iacopo},
	booktitle = {{IEEE} International Conference on eScience, eScience 2025},
	doi = {10.1109/ESCIENCE65000.2025.00037},
	keywords = {icsc},
	location = {Chicago, IL, USA},
	month = sep,
	pages = {250--259},
	publisher = {{IEEE}},
	title = {{BookedSlurm}: meeting user needs for advanced resource reservations in {Slurm}},
	url = {https://iris.unito.it/retrieve/30ac58c8-ecde-4143-8248-e4b0a1da0fe5/IEEE___Slurm.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/30ac58c8-ecde-4143-8248-e4b0a1da0fe5/IEEE___Slurm.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/ESCIENCE65000.2025.00037}}

@inproceedings{25:casella:fed2rc,
	abstract = {Time series classification is a pivotal task in modern machine learning, with widespread applications in fields such as healthcare, finance, and cybersecurity. While deep learning methods dominate recent developments, their resource demands and privacy limitations hinder deployment on low-power and decentralized environments. To address these challenges, we introduce Fed2RC, a fully federated and gradient-free approach that integrates the efficiency of Rocket-based feature extraction with the robustness of ridge regression in a privacy-preserving setting. Fed2RC builds upon two key ideas: (i) federated selection and aggregation of high-performing random convolution kernels, and (ii) incremental and communication-efficient updates of ridge classifier parameters using closed-form solutions. Additionally, we propose a novel federated protocol for selecting the global ridge regularization parameter λ, and show how to improve the communication efficiency by matrix factorization techniques. Extensive experiments on the UCR benchmark demonstrate that Fed2RC achieves state-of-the-art results with a fraction of the computation and communication costs. Code to reproduce the experiments can be found at: https://github.com/CasellaJr/Fed2RC.},
	author = {Casella, Bruno and Fonio, Samuele and Sciandra, Lorenzo and Aldinucci, Marco and Gallicchio, Claudio and Polato, Mirko and Esposito, Roberto},
	booktitle = {28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy -- Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025)},
	doi = {https://dx.doi.org/10.3233/faia251198},
	keywords = {icsc, epi, fl},
	month = oct,
	pages = {3297--3304},
	publisher = {IOS Press},
	title = {Fed2RC: Federated Rocket Kernels and Ridge Classifier for Time Series Classification},
	url = {https://ebooks.iospress.nl/doi/10.3233/FAIA251198},
	year = {2025},
	bdsk-url-1 = {https://ebooks.iospress.nl/doi/10.3233/FAIA251198},
	bdsk-url-2 = {https://dx.doi.org/10.3233/faia251198}}

@inproceedings{25:dagoncapio:escience,
	abstract = {This paper introduces DAGonCAPIO, a workflow framework that integrates the DAGonStar engine with the CAPIO middleware to enable I/O streaming in scientific workflows. DAGonStar utilizes a Directed Acyclic Graph (DAG) to orchestrate tasks. At the same time, CAPIO enables downstream tasks to process data as soon as partial outputs become available, without requiring modifications to application code. This integration reduces delays from traditional file-based communication. The system uses the workflow:// schema to define data dependencies and generate CAPIO coordination scripts. DAGonStar was modified to support early scratch directory naming and decoupled task execution. Experiments with a WRF-based weather forecasting workflow on a 256-core cluster demonstrate that DAGonCAPIO reduces time-to-first-result by up to 4171 seconds, achieving a nearly 10x speedup.},
	author = {Perrotta, Simone and Santimaria, Marco Edoardo and De Vita, Ciro Giuseppe and Torquati, Massimo and Di Luccio, Diana and Corvino, Pasquale and Pirozzi, Antonella and Montella, Raffaele},
	booktitle = {2025 IEEE International Conference on eScience (eScience)},
	doi = {10.1109/escience65000.2025.00047},
	keywords = {capio,icsc},
	month = sep,
	pages = {313--314},
	publisher = {IEEE},
	title = {Directed Acyclic Graph on Cross-Application Programmable I/O: Adding streaming flavour to scientific workflows},
	url = {http://dx.doi.org/10.1109/eScience65000.2025.00047},
	year = {2025},
	bdsk-url-1 = {http://dx.doi.org/10.1109/eScience65000.2025.00047}}

@inproceedings{25:cf:space,
	abstract = {High Performance Computing (HPC) based simulations are crucial in Astrophysics \& Cosmology (A\&C), helping scientists investigate and understand complex astrophysical phenomena. Taking advantage of exascale computing capabilities is essential for these efforts. However, the unprecedented architectural complexity of exascale systems impacts legacy codes. The SPACE Centre of Excellence (CoE) aims to re-engineer key astrophysical codes to tackle new computational challenges by adopting innovative programming paradigms and software (SW) solutions. SPACE brings together scientists, code developers, HPC experts, hardware (HW) manufacturers, and SW developers. This collaboration enhances exascale A&C applications, promoting the use of exascale and post-exascale computing capabilities. Additionally, SPACE addresses high-performance data analysis for the massive data outputs from exascale simulations and modern observations, using machine learning (ML) and visualisation tools. The project facilitates application deployment across platforms by focusing on code repositories and data sharing, integrating European astrophysical communities around exascale computing with standardised SW and data protocols.},
	author = {Shukla, Nitin and Romeo, Alessandro and Caravita, Caterina and Riha, Lubomir and Vysocky, Ondrej and Strakos, Petr and Jaros, Milan and Barbosa, Jo{\~{a}}o and Vavr{\'{\i}}k, Radim and Mignone, Andrea and Rossazza, Marco and Truzzi, Stefano and Berta, Vittoria and Colonnelli, Iacopo and Medi\'{c}, Doriana and Boella, Elisabetta and Gregori, Daniele and Sciacca, Eva and Tornatore, Luca and Taffoni, Giuliano and Deka, Pranab J. and Bacchini, Fabio and Wilhelm, Rostislav{-}Paul and Doulis, Georgios and Pierre, Khalil and Rezzolla, Luciano and Colman, Tine and Commer{\c{c}}on, Beno{\^{\i}}t and Bouizi, Othman and Kuhn, Matthieu and Raffin, Erwan and Sergent, Marc and Wissing, Robert and Marin, Guillermo and Dolag, Klaus and Karademir, Geray S. and Perna, Gino and Zanotti, Marisa and Trujillo{-}Gomez, Sebastian},
	booktitle = {Proceedings of the 22nd {ACM} International Conference on Computing Frontiers: Workshops and Special Sessions, {CF} 2025, Cagliari, Italy, May 28-30, 2025},
	doi = {10.1145/3706594.3728892},
	editor = {Francesca Palumbo and Antonino Tumeo and Ana Lucia Varbanescu and Yogesh Simmhan},
	keywords = {streamflow, space},
	location = {Cagliari, Italy},
	pages = {177--184},
	publisher = {{ACM}},
	title = {{EuroHPC} {SPACE} {CoE}: Redesigning Scalable Parallel Astrophysical Codes for Exascale. Invited Paper},
	url = {https://iris.unito.it/retrieve/f0d21f94-0973-4714-89f5-056e73b2356b/SpaceCF2025Preprint.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/f0d21f94-0973-4714-89f5-056e73b2356b/SpaceCF2025Preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1145/3706594.3728892}}

@inproceedings{25:brescia:systex,
	abstract = {Confidential computing ensures data in-use protection in untrusted cloud environments, yet securing data atrest typically relies on Full Disk Encryption (FDE), which imposes significant performance overhead. This work proposes an alternative in-memory storage approach that eliminates FDE by leveraging SEV-SNP confidential virtual machines (CVMs). Our framework extends SNPGuard, an open-source platform for booting and attesting SEV-SNP VMs, to manage workload execution using temporary file systems (tmpfs), inherently secured by CVM memory encryption. By enabling seamless deployment of Docker based applications, our approach improves runtime and throughput by 20 % on average, with peak gains of 45 % in read-only database workloads. These findings establish in-memory storage as a secure and performant alternative to FDE for handling temporary intermediate data in storage intensive workflows, laying the foundation for future research in this direction.},
	address = {Los Alamitos, CA, USA},
	author = {Brescia, Lorenzo and Colonnelli, Iacopo and Schiavoni, Valerio and Felber, Pascal and Aldinucci, Marco},
	booktitle = {2025 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)},
	doi = {10.1109/EuroSPW67616.2025.00054},
	keywords = {icsc, confidential},
	pages = {414--421},
	publisher = {IEEE Computer Society},
	title = {{ End-To-End Confidentiality with Sev-Snp Leveraging in-Memory Storage }},
	url = {https://iris.unito.it/retrieve/709d45d8-183f-40c6-b486-bb812b4abfa2/systex25-final82.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/709d45d8-183f-40c6-b486-bb812b4abfa2/systex25-final82.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/EuroSPW67616.2025.00054}}

@inproceedings{Antelmi_KDD_2025,
	abstract = {Hypergraphs have emerged as a powerful tool for representing high-order connections in real-world complex systems. Similar to graphs, local structural patterns in hypergraphs, known as high-order motifs (h-motifs), play a crucial role in network dynamics and serve as fundamental building blocks across various domains. For this reason, predicting h-motifs can be highly beneficial in different fields. In this paper, we aim to advance our understanding of such complex high-order dynamics by introducing and formalizing the problem of h-motifs prediction. To address this task, we propose a novel solution that leverages both high-order and pairwise information by combining hypergraph and graph convolutions to capture hyperedges correlation within h-motifs, along with an innovative negative sampling approach designed to generate close-to-positive negative samples. To evaluate the effectiveness of our approach, we defined several baselines inspired by existing literature on hyperedge prediction methods. Our extensive experimental assessments demonstrate that our approach consistently outperforms all the considered baselines, showcasing its superior performance and robustness in predicting h-motifs.},
	address = {New York, NY, USA},
	author = {Antelmi, Alessia and Cordasco, Gennaro and De Vinco, Daniele and Di Pasquale, Valerio and Polato, Mirko and Spagnuolo, Carmine},
	booktitle = {Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1},
	doi = {10.1145/3690624.3709274},
	isbn = {9798400712456},
	keywords = {icsc, analytics},
	location = {Toronto ON, Canada},
	numpages = {12},
	pages = {13--24},
	publisher = {Association for Computing Machinery},
	series = {KDD '25},
	title = {Hypergraph Motif Representation Learning},
	url = {https://doi.org/10.1145/3690624.3709274},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1145/3690624.3709274}}

@inproceedings{Antelmi_MIS4TELwrks_2025,
	abstract = {The growing accessibility of (linked) open data offers a rich opportunity for stakeholders to harness their potential value. Nonetheless, significant obstacles like inadequate technical skills and a lack of awareness hinder their effective utilization. As a result, there is a pressing need for learners to cultivate competencies in data and information literacy, which are integral components of 21st-century skill sets.},
	address = {Cham},
	author = {Antelmi, Alessia and Esposito, Pasquale},
	booktitle = {Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 14th International Conference},
	doi = {10.1007/978-3-031-85561-0_17},
	editor = {Kubincov{\'a}, Zuzana and Dur{\~a}es, Dalila and S{\'a}nchez‑G{\'o}mez, Mar{\'\i}a Cruz and Novais, Paulo and Lancia, Loreto and Pellegrino, Maria Angela},
	isbn = {978-3-031-85561-0},
	keywords = {icsc, analytics},
	pages = {204--215},
	publisher = {Springer Nature Switzerland},
	title = {Empowering Data Literacy Among High School Learners},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-85561-0_17},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/handle/2318/2076250}}

@article{25:TPDS:PPGAIA,
	author = {Malenza, Giulio and Cesare, Valentina and Santimaria, Marco Edoardo and Birke, Robert and Vecchiato, Alberto and Becciani, Ugo and Aldinucci, Marco},
	doi = {10.1109/tpds.2025.3591452},
	issn = {2161-9883},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	keywords = {astro, HPC, dyman, icsc},
	pages = {1--12},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	title = {Performance Portability Assessment in Gaia},
	url = {http://dx.doi.org/10.1109/TPDS.2025.3591452},
	year = {2025},
	bdsk-url-1 = {http://dx.doi.org/10.1109/TPDS.2025.3591452}}

@article{25:FGCS:DagonCapio,
	abstract = {Scientific workflows are increasingly characterized by complex task dependencies and large-scale data exchanges, which place significant pressure on the input/output (I/O) systems of traditional Workflow Engines (WFEs). These challenges are particularly evident in data-intensive and real-time processing contexts, where conventional disk-based I/O mechanisms often become performance bottlenecks. This paper presents an approach to enhancing the DAGonStar scientific workflow engine by integrating CAPIO, a middleware designed to support memory-based streaming I/O. The integration combines DAGonStar's orchestration capabilities with CAPIO's efficient data handling to better support workflows operating on continuous or large-scale datasets. We describe the architectural modifications introduced to enable this collaboration and provide an analysis of the resulting system. The proposed solution aims to improve the responsiveness and flexibility of scientific workflows by streamlining data transfers and simplifying task coordination. This work contributes to the evolution of workflow systems toward more efficient and scalable models for scientific computing.},
	author = {Perrotta, Simone and De Vita, Ciro Giuseppe and Mellone, Gennaro and Santimaria, Marco Edoardo and Torquati, Massimo and Garcia Blas, Javier and Montella, Raffaele},
	doi = {10.1016/j.future.2025.107978},
	issn = {0167-739X},
	journal = {Future Generation Computer Systems},
	keywords = {capio,},
	pages = {107978},
	title = {Streaming I/O for scientific workflow engine acceleration},
	url = {https://www.sciencedirect.com/science/article/pii/S0167739X25002730},
	year = {2025},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0167739X25002730},
	bdsk-url-2 = {https://doi.org/10.1016/j.future.2025.107978}}

@article{25:RISCV64:HLPP,
	abstract = {Massive Data Parallel workloads, driven by inference on large ML models, are pushing hardware vendors to develop efficient and cost-effective multi-core server CPUs. The RISC-V architecture plays a prominent role due to its open, extensible, and energy-friendly ISA. Despite significant progress in recent years, finding efficient methods to run AI applications in parallel on new architectures to fully harness their maximum performance remains a challenge. In this study, we investigate the impact of model parallelism on the inference of machine learning models on the SOPHON SG2042 SoC, the first server-grade CPU based on the RV64 ISA, composed of 64 cores arranged in a grid of 16 groups of 4 cores. Specifically, we aim to enhance performance via better data locality stemming from splitting and assigning parts of the model to specific (groups of) cores handling dependencies via a pipeline execution. We orchestrate execution using FastFlow, a low-level programming framework designed for multithreaded streaming applications. By comparing the results against the standard multi-core inference approach based on data parallelism and analyzing the effects of different submodel-to-core mapping strategies, we aim to provide a comprehensive understanding of how the model parallel approach can maximize efficiency and utilization of hardware resources. In our experiments, using model parallelism improved up to 8.4 times the performance over the native PyTorch parallelism.},
	author = {Malenza, Giulio and Garcia, Adriano Marques and Birke, Robert and Benini, Luca and Aldinucci, Marco},
	date = {2025/06/30},
	date-added = {2025-07-01 00:02:01 +0200},
	date-modified = {2025-07-01 00:02:13 +0200},
	doi = {10.1007/s10766-025-00802-6},
	isbn = {1573-7640},
	journal = {International Journal of Parallel Programming},
	keywords = {icsc, eupilot, dyman, riscv},
	number = {4},
	pages = {27},
	title = {Analysis of Model Parallelism for AI Applications on a 64-core RV64 Server CPU},
	volume = {53},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1007/s10766-025-00802-6}}

@inproceedings{25:dagon-capio-santimaria,
	abstract = {The increasing complexity and scale of data-intensive scientific workflows necessitate advancements in workflow engines (WFEs) to handle real-time data streams and reduce input/output (I/O) bottlenecks. This paper introduces an innovative approach to enhancing the DAGonStar scientific workflow engine by integrating CAPIO, a middleware capable of injecting I/O streaming capabilities into traditional scientific workflows and optimized for high-speed data access and low latency. By combining DAGonStar's robust task orchestration and dependency management with CAPIO, we aim to significantly improve scientific workflows' performance and scalability. We present the design and implementation of this integration, detailing the architectural modifications required to enable seamless interaction between DAGonStar and CAPIO. The paper includes comprehensive benchmarks and performance evaluations demonstrating the impact of CAPIO on workflow execution times and data handling efficiency. Our findings indicate that the enhanced DAGonStar, equipped with CAPIO, offers a powerful solution for managing and processing large-scale, real-time data streams, thereby advancing the capabilities of scientific computing infrastructure.},
	address = {Cham},
	author = {Perrotta, Simone and De Vita, Ciro Giuseppe and Mellone, Gennaro and Santimaria, Marco Edoardo and Salvi, Giuseppe and Lapegna, Marco and Torquati, Massimo and Ciaramella, Angelo},
	booktitle = {Euro-Par 2024: Parallel Processing Workshops},
	doi = {10.1007/978-3-031-90203-1_12},
	isbn = {978-3-031-90203-1},
	keywords = {capio},
	pages = {129--140},
	publisher = {Springer Nature Switzerland},
	title = {Extending a Scientific Workflow Engine with Streaming I/O Capabilities: DAGonStar and CAPIO},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-90203-1_12},
	year = {2025},
	bdsk-url-1 = {https://link.springer.com/chapter/10.1007/978-3-031-90203-1_12},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-031-90203-1_12}}

@inproceedings{DBLP:conf/pdp/MeschiniTGAGA25,
	abstract = {High-performance computing (HPC) data centers are key for advancing research and industry, but their high energy consumption and environmental impact pose critical sustainability challenges. Few HPC centers currently use renewable energy sources, particularly hydrogen-based storage, or integrate waste heat reuse. Recently, Digital Twins (DTs) are proving crucial for advancing sustainable HPC centers, enabling the integration of advanced monitoring and energy optimization with renewable energy sources. They simulate operations, predict maintenance needs, balance workloads, and support renewable integration for enhanced efficiency.},
	author = {Meschini, Silvia and Tagliabue, Lavinia Chiara and Di Giuda, Giuseppe Martino and Aldinucci, Marco and Gasbarri, Paola and Accardo, Daniele},
	booktitle = {33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, {PDP} 2025, Turin, Italy, March 12-14, 2025},
	date-modified = {2026-08-14 12:12:49 +0200},
	doi = {10.1109/PDP66500.2025.00073},
	keywords = {DCs, dyman},
	pages = {471--477},
	publisher = {{IEEE}},
	title = {Sustainable-HPC: toward Digital Twin for active management of self-cooled data centers with Renewable Energy Sources and waste heat recovery},
	url = {https://iris.unito.it/retrieve/b863d9b9-900a-485d-a14e-7830be62a5bb/Sustainable\%20HPC_full_rev_.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/b863d9b9-900a-485d-a14e-7830be62a5bb/Sustainable%5C%20HPC_full_rev_.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/PDP66500.2025.00073}}

@article{10.1145/3729215,
	abstract = {Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and classifies the most recent developments in DL accelerators, focusing on their role in meeting the performance demands of HPC applications. We explore cutting-edge approaches to DL acceleration, covering not only GPU- and TPU-based platforms but also specialized hardware such as FPGA- and ASIC-based accelerators, Neural Processing Units, open hardware RISC-V-based accelerators, and co-processors. This survey also describes accelerators leveraging emerging memory technologies and computing paradigms, including 3D-stacked Processor-In-Memory, non-volatile memories like Resistive RAM and Phase Change Memories used for in-memory computing, as well as Neuromorphic Processing Units, and Multi-Chip Module-based accelerators. Furthermore, we provide insights into emerging quantum-based accelerators and photonics. Finally, this survey categorizes the most influential architectures and technologies from recent years, offering readers a comprehensive perspective on the rapidly evolving field of deep learning acceleration.},
	address = {New York, NY, USA},
	articleno = {286},
	author = {Silvano, Cristina and Ielmini, Daniele and Ferrandi, Fabrizio and Fiorin, Leandro and Curzel, Serena and Benini, Luca and Conti, Francesco and Garofalo, Angelo and Zambelli, Cristian and Calore, Enrico and Schifano, Sebastiano and Palesi, Maurizio and Ascia, Giuseppe and Patti, Davide and Petra, Nicola and De Caro, Davide and Lavagno, Luciano and Urso, Teodoro and Cardellini, Valeria and Cardarilli, Gian Carlo and Birke, Robert and Perri, Stefania},
	doi = {10.1145/3729215},
	issn = {0360-0300},
	issue_date = {November 2025},
	journal = {ACM Comput. Surv.},
	keywords = {icsc, riscv},
	month = jun,
	number = {11},
	numpages = {39},
	publisher = {Association for Computing Machinery},
	title = {A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms},
	url = {https://doi.org/10.1145/3729215},
	volume = {57},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1145/3729215}}

@inproceedings{lek:SAM:ICMLW25,
	author = {Lek, Gert and Zhu, Chaoyi and Chen, Pin-Yu and Birke, Robert and Chen, Lydia Y.},
	booktitle = {ICML 2025 Workshop DIG-BUG},
	keywords = {icsc},
	title = {Detective {SAM}: Adapting {SAM} to Localize Diffusion-based Forgeries via Embedding Artifacts},
	url = {https://openreview.net/forum?id=GPpFTmGLgT},
	year = {2025},
	bdsk-url-1 = {https://openreview.net/forum?id=GPpFTmGLgT}}

@inproceedings{Zhao:Tabula,
	author = {Zhao, Zilong and Birke, Robert and Chen, Lydia Y.},
	booktitle = {Advances in Knowledge Discovery and Data Mining - 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, {PAKDD} 2025},
	doi = {10.1007/978-981-96-8186-0\_20},
	pages = {247--259},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	title = {TabuLa: Harnessing Language Models for Tabular Data Synthesis},
	volume = {15874},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1007/978-981-96-8186-0%5C_20}}

@inproceedings{25:DBLP:conf/pdp/VaccaroTA25,
	author = {Viviana Vaccaro and Lavinia Chiara Tagliabue and Marco Aldinucci},
	booktitle = {33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, {PDP} 2025, Turin, Italy, March 12-14, 2025},
	date-modified = {2026-08-14 12:12:33 +0200},
	doi = {10.1109/PDP66500.2025.00075},
	keywords = {DCs, HPC, dyman},
	pages = {486--493},
	publisher = {{IEEE}},
	title = {Sustainable Data Centers: Advancing Energy Efficiency and Resource Optimization},
	url = {https://iris.unito.it/retrieve/b6a43c6c-1429-45bb-a6bd-f0e3178cc61b/2025_PDP_sustainable_DC_postprint.pdf},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1109/PDP66500.2025.00075}}

@inproceedings{25:gaia:pdp,
	author = {Valentina Cesare and Ugo Becciani and Alberto Vecchiato and Mario Gilberto Lattanzi and Marco Aldinucci and Beatrice Bucciarelli},
	booktitle = {33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, {PDP} 2025, Turin, Italy, March 12-14, 2025},
	doi = {10.1109/PDP66500.2025.00063},
	keywords = {astro, HPC, icsc, eupex},
	pages = {404--411},
	publisher = {{IEEE}},
	title = {Covariances computation in the Gaia {AVU-GSR} Parallel Solver with {I/O} techniques: a performance study as a function of writing cycle length},
	url = {https://doi.org/10.1109/PDP66500.2025.00063},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1109/PDP66500.2025.00063}}

@inproceedings{imbrosciano:2025:pdp,
	abstract = {The HaMMon project is the outcome of an industrial partnership that includes many Italian research institutions and private companies. It is led by UnipolSai and Leitha, and funded by the ICSC, the Italian National Research Center for High Performance Computing, Big Data and Quantum Computing.The ambition of HaMMon is to build a flexible and scalable platform to analyze the hydrogeological and atmospheric balance of the Italian territory. The project aims to expand the current knowledge in hazard mapping, monitoring, and forecasting from an industrial perspective by leveraging innovative technologies and the interdisciplinary activities carried out by the ICSC.In this work, we present the cloud-HPC infrastructure deployed in the High-Performance Computing for Artificial Intelligence (HPC4AI) green data center of the University of Turin which supports the testing and development of HaMMon's applications and services. We describe the current activities and preliminary results related to the integration of Photogrammetry techniques, Data Visualization and Artificial Intelligence technologies, applied on aerial images, to assess extreme natural events and evaluate their impact on risk-exposed assets.},
	author = {Mauro Imbrosciano and Eva Sciacca and Fabio Vitello and Leonardo Pelonero and Francesco Franchina and Ugo Becciani and Iacopo Colonnelli and Doriana Medi\'{c}},
	booktitle = {2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},
	date-modified = {2025-05-24 09:13:03 +0200},
	doi = {10.1109/PDP66500.2025.00050},
	issn = {2377-5750},
	keywords = {system-sw, hpc4ai, icsc, jupyter-workflow, streamflow},
	pages = {309--316},
	publisher = {IEEE},
	title = {The Cloud-HPC infrastructure for Hazard Mapping and vulnerability Monitoring (HaMMon)},
	url = {https://iris.unito.it/retrieve/36a223d5-7134-4469-80a0-705c0060a5d4/PDP_2025_HaMMON.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/36a223d5-7134-4469-80a0-705c0060a5d4/PDP_2025_HaMMON.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/PDP66500.2025.00050}}

@inproceedings{tuccari:2025:pdp,
	abstract = {Modern Astrophysics and Cosmology (A\&C) projects produce immense data volumes, necessitating advanced software tools for data access, storage, and analysis. Visualization Interface for the Virtual Observatory (VisIVO) is one such tool enabling multi-dimensional data analysis and knowledge discovery across complex astrophysical datasets. Leveraging containerization and virtualization, VisIVO has been deployed on various distributed computing platforms. Additionally, Blender, an open-source 3D suite, provides robust tools for rendering and processing volumetric data, making it suitable for visualizing complex datasets. At the SPACE Center of Excellence these tools are being adapted for high-performance visualization of cosmological simulations performed with GADGET and ChaNGa on pre-exascale systems. However, implementing high-performance visualization on diverse HPC platforms presents several challenges, including hardware and software compatibility, data management, scalability, performance portability, and efficient resource allocation. This paper outlines strategies to integrate VisIVO with workflow frameworks and streaming platforms to address these challenges. Workflow frameworks enhance portability, scheduling, and reproducibility of visualization workflows on pre-exascale systems used in A\&C simulations. We also discuss the use of streaming platforms to enable concurrent (i.e. in-situ) analysis and visualization of simulations, reducing the need to store full simulation data by leveraging distributed databases that stream the output data in real time. Lastly, we present an adaptation of Blender to handle large-scale particle-based astrophysical data, offering high-quality visualization with interactive exploration capabilities.},
	author = {Tuccari, Nicola and Sciacca, Eva and Vitello, Fabio and Colonnelli, Iacopo and Becerra, Yolanda and Cintero, Enric Sosa and Marin, Guillermo and Jaros, Milan and Riha, Lubomir and Strakos, Petr and Trujillo-Gomez, Sebastian and Tramontana, Emiliano and Wissing, Robert},
	booktitle = {2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},
	doi = {10.1109/PDP66500.2025.00069},
	issn = {2377-5750},
	keywords = {icsc, space, streamflow},
	pages = {443--450},
	publisher = {IEEE},
	title = {High Performance Visualization for Astrophysics and Cosmology},
	url = {https://iris.unito.it/retrieve/994fe11b-b282-4267-80c9-6d60821ba2cb/PDP2025___WP3_SPACE_Vis.pdf},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/994fe11b-b282-4267-80c9-6d60821ba2cb/PDP2025___WP3_SPACE_Vis.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/PDP66500.2025.00069}}

@article{chisari2025cloud,
	abstract = {Accurate cloud detection is critical for advancing atmospheric monitoring and meteorological forecasting. This paper presents the Cloud Detection Challenge, an initiative aimed at enhancing cloud detection through innovative solutions using lidar-based ceilometer data. This initiative was hosted by IEEE MetroXRAINE 2024, and 11 teams participated in this initiative. Participants were provided with a novel dataset of backscatter profiles converted into time-height plots, offering unique insights into atmospheric conditions beyond conventional imagery. Data collection employed a Lufft CHM 15k ceilometer, capturing cloud dynamics every 15 seconds located near Mt. Etna, an active volcano in Italy. The dataset includes 1568 hourly labeled backscatter profiles, serving as a benchmark for state-of-the-art deep learning models. The challenge sets a baseline performance of 89.57\% accuracy, 92.73\% F1-score, 89.82\% precision, and 95.84\% recall, inviting participants to develop models to exceed these results. Submissions proposed a widerange of AI-based approaches, including Transformer and Convolutional Neural Network architectures, showcasing the potential of advanced image analysis techniques in lidar-based cloud detection. This paper details the challenge framework, as well as the methodologies proposed by top-performing teams, offering a comparative evaluation of their effectiveness. Our initiative advances cloud detection technologies and underscores their broader implications for environmental monitoring, agriculture, and satellite imaging. The insights and dataset presented herein lay the groundwork for future advancements in leveraging lidar data for atmospheric analysis.},
	author = {Barbaro Chisari, Alessio and Guarnera, Luca and Ortis, Alessandro and Patatu, Wladimiro Carlo and Casella, Bruno and Naso, Luca and Puglisi, Giuseppe and Del Zoppo, Vincenzo and Giuffrida, Mario Valerio and Battiato, Sebastiano},
	date-modified = {2025-05-24 09:17:18 +0200},
	doi = {10.1109/ACCESS.2025.3553422},
	journal = {IEEE Access},
	keywords = {ai},
	pages = {53394--53406},
	title = {Cloud Detection Challenge - Methods and Results},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10934992},
	volume = {13},
	year = {2025},
	bdsk-url-1 = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10934992}}

@article{casella2025performance,
	abstract = {Federated Learning (FL) is a distributed machine learning approach that has emerged as an effective way to address recent privacy concerns. However, FL introduces the need for additional security measures as FL alone is still subject to vulner- abilities such as model and data poisoning and inference attacks. Confidential Computing (CC) is a paradigm that, by leveraging hardware-based trusted execution environments (TEEs), protects the confidentiality and integrity of ML models and data, thus resulting in a powerful ally of FL applications. Typical TEEs offer an application-isolation level but suffer many drawbacks, such as limited available memory and debugging and coding difficulties. The new generation of TEEs offers a virtual machine (VM)- based isolation level, thus reducing the porting effort for existing applications. In this work, we compare the performance of VM- based and application-isolation level TEEs for confidential FL (CFL) applications. In particular, we evaluate the impact of TEEs and additional security mechanisms such as TLS (for securing the communication channel). The results, obtained across three datasets and two deep learning models, demonstrate that the new VM-based TEEs introduce a limited overhead (at most 1.5x), thus paving the way to leverage public and untrusted computing environments, such as HPC facilities or public cloud, without detriment to performance.},
	address = {Turin, Italy},
	author = {Bruno Casella},
	booktitle = {2025 33rd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
	doi = {10.1109/PDP66500.2025.00035},
	keywords = {icsc, epi},
	pages = {204--208},
	title = {A performance analysis of VM-based Trusted Execution Environments for Confidential Federated Learning},
	url = {https://iris.unito.it/retrieve/4cd172c3-dee9-4c0c-9f34-7bcbae584571/PDP25___A_performance_comparison_of_Confidential_Computing_architectures_for_Federated_Learning_tasks.pdf},
	year = {2025},
	bdsk-url-1 = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10974758},
	bdsk-url-2 = {https://doi.org/10.1109/PDP66500.2025.00035}}

@inproceedings{vaccar0:pdp25,
	abstract = {The HPC4AI datacenter hosted at the Computer Science Department of the University of Turin was born to address the exponentially increasing computing needs of cross-disciplinary research on AI. To address the needs of modern AI, HPC4AI rethinks the traditional usage of Cloud and HPC systems where the Cloud provides a modern interface for HPC, and HPC serves as an accelerator for the cloud. To date, it has supported 40+ research projects across a broad range of domains, from astronomy and medicine to human sciences. Furthermore, it acts as an R\&D platform to study, develop, and test new datacenter technologies. As such, it hosts a zoo of exotic computing platforms and the first prototype of two-phase evaporative server cooling. This work describes operating and managing HPC4AI with its challenges and lessons learned, with an analysis of key opportunities for digital twins.},
	author = {Vaccaro, Viviana and Birke, Robert and Tagliabue, Lavinia Chiara and Rabellino, Sergio and Aldinucci, Marco},
	booktitle = {2025 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)},
	date-modified = {2026-08-14 12:13:05 +0200},
	doi = {10.1109/PDP66500.2025.00077},
	keywords = {DCs, icsc, dyman},
	pages = {499--505},
	title = {HPC4AI@UNITO: A Use Case For Datacenter Digital Twin},
	url = {https://iris.unito.it/retrieve/652d1a4f-69fd-4690-a3b0-9d5723b4db4b/PDP25_DT4DC_HPC4AI.pdf},
	year = {2025},
	bdsk-url-1 = {https://ieeexplore.ieee.org/document/10974756},
	bdsk-url-2 = {https://doi.org/10.1109/PDP66500.2025.00077}}

@inproceedings{zhu2025:iclr,
	abstract = {Synthetic data offers alternatives for data augmentation and sharing. Till date, it remains unknown how to use watermarking techniques to trace and audit synthetic tables generated by tabular diffusion models to mitigate potential misuses. In this paper, we design TabWak, the first watermarking method to embed invisible signatures that control the sampling of Gaussian latent codes used to synthesize table rows via the diffusion backbone. TabWak has two key features. Different from existing image watermarking techniques, TabWak uses self-cloning and shuffling to embed the secret key in positional information of random seeds that control the Gaussian latents, allowing to use different seeds at each row for high inter-row diversity and enabling row-wise detectability. To further boost the robustness of watermark detection against post-editing attacks, TabWak uses a valid-bit mechanism that focuses on the tail of the latent code distribution for superior noise resilience. We provide theoretical guarantees on the row diversity and effectiveness of detectability. We evaluate TabWak on five datasets against baselines to show that the quality of watermarked tables remains nearly indistinguishable from non-watermarked tables while achieving high detectability in the presence of strong post-editing attacks, with a 100\% true positive rate at a 0.1\% false positive rate on synthetic tables with fewer than 300 rows. Our code is available at the following repository https://github.com/chaoyitud/TabWak.},
	author = {Zhu, Chaoyi and Tang, Jiayi and Galjaard, Jeroen and Chen, Pin-Yu and Birke, Robert and Bos, Cornelis and Chen, Lydia Y.},
	booktitle = {The Thirteenth International Conference on Learning Representations},
	keywords = {icsc, dyman},
	title = {TabWak: A Watermark for Tabular Diffusion Models},
	url = {https://openreview.net/forum?id=71pur4y8gs},
	year = {2025},
	bdsk-url-1 = {https://openreview.net/forum?id=71pur4y8gs}}

@article{icolonne:eurohpc:24,
	abstract = {The rapid advancements in AI and Machine Learning necessitate a robust computational infrastructure to support cutting-edge research and industrial applications. From the academic and industrial AI community perspective, voiced in the recent ELISE project, the European AI platform is recommended to center around the EuroHPC growing ecosystem. It should be user-driven, easily accessible, powerful, and compliant with European regulations. AI-optimized and dedicated supercomputers for the European AI community are also coming, in addition to upgrading partitions of existing EuroHPC systems to 'AI enabled' stage. Related calls have been initiated in September 2024. Further, conventional EuroHPC systems are suggested to be extended with quantum computing, edge AI, and neuromorphic computing to cater to AI models deployed on network edge devices and sustainability in the long run. The challenges are presented in three case studies, ranging from training Transformers on HPC to LLMs trained federally across three different Euro HPC systems to recent results on hybrid classical-quantum application. This paper concludes with case studies results-informed next steps believed to benefit AI practitioners and the broader AI community.},
	address = {Amsterdam, Netherlands},
	author = {Taborsky, Petr and Colonnelli, Iacopo and Kurowski, Krzysztof and Sarma, Rakesh and Pontoppidan, Niels Henrik and Jans{\'\i}k, Branislav and Detlefsen, Nicki Skafte and Egholm Pedersen, Jens and Larsen, Rasmus and Hansen, Lars Kai},
	booktitle = {Proceedings of the Second EuroHPC user day},
	doi = {10.1016/j.procs.2025.02.269},
	issn = {1877-0509},
	journal = {Procedia Computer Science},
	keywords = {streamflow},
	pages = {140--149},
	publisher = {Elsevier},
	title = {Towards a European {HPC}/{AI} ecosystem: a community-driven report},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050925006301},
	volume = {255},
	year = {2025},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S1877050925006301},
	bdsk-url-2 = {https://doi.org/10.1016/j.procs.2025.02.269}}

@inproceedings{santimaria_capio_cl_25,
	abstract = {The performance bottleneck in file-based workflows remains a pressing issue in the realm of I/O-based workflows. To address this challenge, a novel annotation language has been developed. CAPIO-CL is positioned as an innovative I/O coordination language, enabling users to annotate data dependencies within file-based workflows with synchronization semantics pertinent to the involved files and directories. Through the information provided by the language, optimization opportunities arise in streaming and preemptive data movement. This paper serves to illustrate the semantics and syntax enabling CAPIO-CL to enhance the performance of in situ workflows without necessitating the rewriting or modification of the original workflow application steps. Finally, an analysis of CAPIO-CL is provided, taking into consideration both language expressiveness and application performance enhancement.},
	author = {Santimaria, Marco Edoardo and Martinelli, Alberto R. and Colonnelli, Iacopo and Cantalupo, Barbara and Torquati, Massimo and Aldinucci, Marco},
	date = {2025/02/25},
	date-added = {2025-02-25 19:59:05 +0100},
	date-modified = {2025-02-25 20:00:33 +0100},
	doi = {10.1007/s10766-025-00789-0},
	id = {Santimaria2025},
	isbn = {1573-7640},
	journal = {International Journal of Parallel Programming},
	keywords = {capio, icsc},
	number = {2},
	pages = {10},
	title = {CAPIO-CL: The CAPIO Coordination Language},
	url = {https://doi.org/10.1007/s10766-025-00789-0},
	volume = {53},
	year = {2025},
	bdsk-url-1 = {https://doi.org/10.1007/s10766-025-00789-0}}

@inproceedings{24:fm:swirl,
	abstract = {In the ever-evolving landscape of scientific computing, properly supporting the modularity and complexity of modern scientific applications requires new approaches to workflow execution, like seamless interoperability between different workflow systems, distributed-by-design workflow models, and automatic optimisation of data movements. In order to address this need, this article introduces SWIRL, an intermediate representation language for scientific workflows. In contrast with other product-agnostic workflow languages, SWIRL is not designed for human interaction but to serve as a low-level compilation target for distributed workflow execution plans. The main advantages of SWIRL semantics are low-level primitives based on the send/receive programming model and a formal framework ensuring the consistency of the semantics and the specification of translating workflow models represented by Directed Acyclic Graphs (DAGs) into SWIRL workflow descriptions. Additionally, SWIRL offers rewriting rules designed to optimise execution traces, accompanied by corresponding equivalence. An open-source SWIRL compiler toolchain has been developed using the ANTLR Python3 bindings.},
	address = {Milano, Italy},
	author = {Iacopo Colonnelli and Doriana Medi\'{c} and Alberto Mulone and Viviana Bono and Luca Padovani and Marco Aldinucci},
	booktitle = {Formal Methods. FM 2024},
	doi = {10.1007/978-3-031-71162-6_12},
	editor = {Andr{\'e} Platzer and Kristin Yvonne Rozier and Matteo Pradella and Matteo Rossi},
	keywords = {icsc, eupex},
	month = sep,
	pages = {226--244},
	publisher = {Springer Nature Switzerland},
	series = {Lecture Notes in Computer Science},
	title = {Introducing {SWIRL}: An Intermediate Representation Language for Scientific Workflows},
	url = {https://iris.unito.it/retrieve/b39a6f09-a8d3-4974-abf6-c109916694fa/PDFEditoriale.pdf},
	volume = {14933},
	year = {2025},
	bdsk-url-1 = {https://iris.unito.it/retrieve/b39a6f09-a8d3-4974-abf6-c109916694fa/PDFEditoriale.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-031-71162-6_12}}

@inproceedings{23:itadata:mittone:fonio,
	abstract = {Federated learning (FL) is a distributed machine learning paradigm allowing cooperative model training between multiple parties while maintaining local data privacy. FL can be deployed at various scales, ranging from thousands of low-end devices (e.g., smartphones) to just a few high-performance infrastructures (e.g., HPCs), raising critical concerns about the scalability of state-of-the-art FL frameworks. This preliminary study evaluates the scaling performance of a representative FL framework, i.e., Flower, in high-performance controlled environments, providing insights on such frameworks from a computational performance point of view. Two public Top500 pre- exascale HPC infrastructures are exploited to obtain reliable and comparable results: Leonardo and MareNostrum5. Our findings suggest that the design of current FL frameworks, and especially their communication backends, may overlook computational performance, leading to poor scaling in large-scale scenarios.},
	address = {Turin, Italy},
	author = {Mittone, Gianluca and Fonio, Samuele and Birke, Robert and Aldinucci, Marco},
	booktitle = {Proceedings of the 3rd Special Track on Big Data and High-Performance Computing (BigHPC 2025) co-located with the 4th Italian Conference on Big Data and Data Science (ITADATA 2025), Turin, Italy, September 9-11, 2025.},
	issn = {1613-0073},
	keywords = {icsc, space, hpc, fl},
	month = sep,
	pages = {1--5},
	publisher = {{CEUR}},
	title = {Benchmarking Federated Learning Frameworks' Scalability},
	url = {https://hdl.handle.net/2318/1933852},
	urn = {urn:nbn:de:0074-4124-x},
	volume = {4124},
	year = {2025},
	bdsk-url-1 = {https://hdl.handle.net/2318/1933852}}

@inproceedings{25:aistats:fonio,
	abstract = {Non-Euclidean geometries have garnered significant research interest, particularly in their application to Deep Learning. Utilizing specific manifolds as embedding spaces has been shown to enhance neural network representational capabilities by aligning these spaces with the data's latent structure. In this paper, we focus on hyperbolic manifolds and introduce a novel framework, Hyperbolic Prototypical Entailment Cones (HPEC). The core innovation of HPEC lies in utilizing angular relationships, rather than traditional distance metrics, to more effectively capture the similarity between data representations and their corresponding prototypes. This is achieved by leveraging hyperbolic entailment cones, a mathematical construct particularly suited for embedding hierarchical structures in the Poincare' Ball, along with a novel Backclip mechanism. Our experimental results demonstrate that this approach significantly enhances performance in high-dimensional embedding spaces. To substantiate these findings, we evaluate HPEC on four diverse datasets across various embedding dimensions, consistently surpassing state-of-the-art methods in Prototype Learning.},
	address = {Mai Khao, Thailand},
	author = {Fonio, Samuele and Esposito, Roberto and Aldinucci, Marco},
	booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
	keywords = {icsc, ai},
	month = may,
	title = {Hyperbolic Prototypical Entailment Cones for Image Classification},
	url = {https://proceedings.mlr.press/v258/fonio25a.html},
	year = {2025},
	bdsk-url-1 = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/fonio25a/fonio25a.pdf}}

@article{DBLP:journals/jlap/BarbaneraBD25,
  author       = {Franco Barbanera and
                  Viviana Bono and
                  Mariangiola Dezani{-}Ciancaglini},
  title        = {Partially typed multiparty sessions with internal delegation},
  journal      = {J. Log. Algebraic Methods Program.},
  volume       = {142},
  pages        = {101018},
  year         = {2025},
  url          = {https://doi.org/10.1016/j.jlamp.2024.101018},
  doi          = {10.1016/J.JLAMP.2024.101018},
  timestamp    = {Mon, 09 Dec 2024 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/jlap/BarbaneraBD25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/jlap/BarbaneraBD25a,
  author       = {Franco Barbanera and
                  Viviana Bono and
                  Mariangiola Dezani{-}Ciancaglini},
  title        = {Open compliance in multiparty sessions with partial typing},
  journal      = {J. Log. Algebraic Methods Program.},
  volume       = {144},
  pages        = {101046},
  year         = {2025},
  url          = {https://doi.org/10.1016/j.jlamp.2025.101046},
  doi          = {10.1016/J.JLAMP.2025.101046},
  timestamp    = {Tue, 04 Mar 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/jlap/BarbaneraBD25a.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tcs/BarileBR25,
  author       = {Adriano Barile and
                  Stefano Berardi and
                  Luca Roversi},
  title        = {Termination of rewriting on reversible Boolean circuits as a free
                  3-category problem},
  journal      = {Theor. Comput. Sci.},
  volume       = {1028},
  pages        = {115031},
  year         = {2025},
  url          = {https://doi.org/10.1016/j.tcs.2024.115031},
  doi          = {10.1016/J.TCS.2024.115031},
  timestamp    = {Sat, 25 Jan 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/tcs/BarileBR25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/bighpc/BifulcoR25,
  author       = {Mario Bifulco and
                  Luca Roversi},
  editor       = {Alessia Antelmi and
                  Massimo Cafaro and
                  Italo Epicoco and
                  Marco Pulimeno},
  title        = {Exploring an implementation of quantum learning pipeline for support
                  vector machines},
  booktitle    = {Proceedings of the 3rd Special Track on Big Data and High-Performance
                  Computing (BigHPC 2025) co-located with the 4th Italian Conference
                  on Big Data and Data Science {(ITADATA} 2025), Turin, Italy, September
                  9-11, 2025},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {4124},
  pages        = {1--6},
  publisher    = {CEUR-WS.org},
  year         = {2025},
  url          = {https://ceur-ws.org/Vol-4124/paper16.pdf},
  timestamp    = {Tue, 27 Jan 2026 17:17:18 +0100},
  biburl       = {https://dblp.org/rec/conf/bighpc/BifulcoR25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/rc/PalazzoR25,
  author       = {Matteo Palazzo and
                  Luca Roversi},
  editor       = {Robert Gl{\"{u}}ck and
                  Robin Kaarsgaard},
  title        = {Towards a Characterization of Two-Way Bijections in a Reversible Computational
                  Model},
  booktitle    = {Reversible Computation - 17th International Conference, {RC} 2025,
                  Odense, Denmark, July 3-4, 2025, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {15716},
  pages        = {128--136},
  publisher    = {Springer},
  year         = {2025},
  url          = {https://doi.org/10.1007/978-3-031-97063-4\_10},
  doi          = {10.1007/978-3-031-97063-4\_10},
  timestamp    = {Sun, 06 Jul 2025 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/rc/PalazzoR25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2511-03327,
  author       = {Mario Bifulco and
                  Luca Roversi},
  title        = {Exploring Topologies in Quantum Annealing: {A} Hardware-Aware Perspective},
  journal      = {CoRR},
  volume       = {abs/2511.03327},
  year         = {2025},
  url          = {https://doi.org/10.48550/arXiv.2511.03327},
  doi          = {10.48550/ARXIV.2511.03327},
  eprinttype   = {arXiv},
  eprint       = {2511.03327},
  timestamp    = {Fri, 02 Jan 2026 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2511-03327.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/snam/AntelmiCP25,
  author       = {Alessia Antelmi and
                  Lucio La Cava and
                  Arianna Pera},
  title        = {Characterizing swing voters in online social media: the case of the
                  2022 Italian elections},
  journal      = {Soc. Netw. Anal. Min.},
  volume       = {15},
  number       = {1},
  pages        = {77},
  year         = {2025},
  url          = {https://doi.org/10.1007/s13278-025-01457-4},
  doi          = {10.1007/S13278-025-01457-4},
  timestamp    = {Thu, 11 Sep 2025 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/snam/AntelmiCP25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/cikm/KimL0APS25,
  author       = {Sunwoo Kim and
                  Soo Yong Lee and
                  Yue Gao and
                  Alessia Antelmi and
                  Mirko Polato and
                  Kijung Shin},
  editor       = {Meeyoung Cha and
                  Chanyoung Park and
                  Noseong Park and
                  Carl Yang and
                  Senjuti Basu Roy and
                  Jessie Li and
                  Jaap Kamps and
                  Kijung Shin and
                  Bryan Hooi and
                  Lifang He},
  title        = {A Tutorial on Hypergraph Neural Networks: An In-Depth and Step-By-Step
                  Guide},
  booktitle    = {Proceedings of the 34th {ACM} International Conference on Information
                  and Knowledge Management, {CIKM} 2025, Seoul, Republic of Korea, November
                  10-14, 2025},
  pages        = {6829--6832},
  publisher    = {{ACM}},
  year         = {2025},
  url          = {https://doi.org/10.1145/3746252.3761449},
  doi          = {10.1145/3746252.3761449},
  timestamp    = {Wed, 11 Mar 2026 15:53:27 +0100},
  biburl       = {https://dblp.org/rec/conf/cikm/KimL0APS25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/iwec/LoriaASP25,
  author       = {Enrica Loria and
                  Alessia Antelmi and
                  Carmine Spagnuolo and
                  Johanna Pirker},
  editor       = {Maki Sugimoto and
                  Angelo Di Iorio and
                  Pablo A. Figueroa and
                  Ryosuke Yamanishi and
                  Kohei Matsumura},
  title        = {Stay and Play - Modeling Social Influence of Players in the Steam
                  Gaming Community},
  booktitle    = {Entertainment Computing - {ICEC} 2025 - 24th {IFIP} {TC} 14 International
                  Conference, Tokyo, Japan, August 27-30, 2025, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {16042},
  pages        = {263--279},
  publisher    = {Springer},
  year         = {2025},
  url          = {https://doi.org/10.1007/978-3-032-02555-5\_19},
  doi          = {10.1007/978-3-032-02555-5\_19},
  timestamp    = {Sat, 13 Sep 2025 16:18:23 +0200},
  biburl       = {https://dblp.org/rec/conf/iwec/LoriaASP25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/bighpc/2025,
  editor       = {Alessia Antelmi and
                  Massimo Cafaro and
                  Italo Epicoco and
                  Marco Pulimeno},
  title        = {Proceedings of the 3rd Special Track on Big Data and High-Performance
                  Computing (BigHPC 2025) co-located with the 4th Italian Conference
                  on Big Data and Data Science {(ITADATA} 2025), Turin, Italy, September
                  9-11, 2025},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {4124},
  publisher    = {CEUR-WS.org},
  year         = {2025},
  url          = {https://ceur-ws.org/Vol-4124},
  urn          = {urn:nbn:de:0074-4124-x},
  timestamp    = {Tue, 27 Jan 2026 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/bighpc/2025.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{DBLP:data/11/AlmeidaAGP25,
  author       = {Jussara M. Almeida and
                  Alessia Antelmi and
                  Marcos Andr{\'{e}} Gon{\c{c}}alves and
                  Maria Angela Pellegrino},
  title        = {The Complex Ecosystem of Hyperprolific Authors - Replication Package
                  (Version 1)},
  publisher    = {Zenodo},
  year         = {2025},
  month        = sep,
  howpublished = {\url{https://doi.org/10.5281/zenodo.17051997}},
  note         = {Accessed on YYYY-MM-DD.},
  url          = {https://doi.org/10.5281/zenodo.17051997},
  doi          = {10.5281/ZENODO.17051997},
  timestamp    = {Mon, 18 May 2026 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/data/11/AlmeidaAGP25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{DBLP:data/12/AlmeidaAGP25,
  author       = {Jussara M. Almeida and
                  Alessia Antelmi and
                  Marcos Andr{\'{e}} Gon{\c{c}}alves and
                  Maria Angela Pellegrino},
  title        = {The Complex Ecosystem of Hyperprolific Authors - Replication Package
                  (Version 2)},
  publisher    = {Zenodo},
  year         = {2025},
  month        = dec,
  howpublished = {\url{https://doi.org/10.5281/zenodo.18037414}},
  note         = {Accessed on YYYY-MM-DD.},
  url          = {https://doi.org/10.5281/zenodo.18037414},
  doi          = {10.5281/ZENODO.18037414},
  timestamp    = {Mon, 18 May 2026 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/data/12/AlmeidaAGP25.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{2025:hipes:fogliobonda,
	title        = {Accelerating {SWIRL} Workflows: A High-Performance {Rust} Backend for Distributed Execution},
	author       = {Foglio Bonda, Tommaso and Medi{\'c}, Doriana and Mulone, Alberto and Aldinucci, Marco},
	year         = 2025,
	month        = aug,
	booktitle    = {Euro-Par 2025: Parallel Processing Workshops (HiPES 2025)},
	publisher    = {Springer Nature Switzerland},
	address      = {Brescia, Italy},
	series       = {Lecture Notes in Computer Science},
	volume       = 16294,
	keywords     = {swirl, streamflow, icsc, eupex}
}

@inproceedings{Medina2024KyberRust,
  author    = {Medina, Francesco and Molteni, Maria Chiara and Di Scala, Antonio Jose and Nava, Lorenzo},
  title     = {Analysis and Contributions to an Open Source {Kyber} Library in {Rust}},
  booktitle = {2024 {IEEE} 29th Symposium on Computers and Communications ({ISCC})},
  pages     = {1--6},
  year      = {2024},
  doi       = {10.1109/ISCC61673.2024.10733712},
  url       = {https://ieeexplore.ieee.org/document/10733712}
}

@inproceedings{24:gaia:avugsr,
	abstract = {We GPU ported with CUDA the solver module of the Astrometric Verification Unit--Global Sphere Reconstruction (AVU-GSR) pipeline for the ESA Gaia mission. The code finds the astrometric parameters of 108 sources, by solving a linear system with the LSQR. The coefficient matrix is large (10-50 TB) and sparse. The CUDA code accelerates over the original MPI + OpenMP solver of 14x on CINECA cluster Marconi100. We migrated the code production to Leonardo, which has 4x GPU memory per node.},
	author = {Cesare, Valentina and Becciani, Ugo and Vecchiato, Alberto and Lattanzi, Mario Gilberto and Aldinucci, Marco and Bucciarelli, Beatrice},
	booktitle = {Software and Cyberinfrastructure for Astronomy VIII},
	date-added = {2025-05-24 09:30:44 +0200},
	date-modified = {2025-05-24 10:22:20 +0200},
	doi = {10.1117/12.3018102},
	editor = {SPIE},
	keywords = {astro, HPC},
	month = jul,
	pages = {61},
	publisher = {SPIE},
	title = {The Gaia AVU--GSR solver: CPU+GPU parallel solutions for linear systems solving and covariances calculation toward Exascale systems},
	url = {http://dx.doi.org/10.1117/12.3018102},
	year = {2024},
	bdsk-url-1 = {http://dx.doi.org/10.1117/12.3018102}}

@inproceedings{Malenza_P3HPC_24,
	abstract = {Applications that analyze data from modern scientific experiments will soon require a computing capacity of ExaFLOPs. The current trend to achieve such performance is to employ GPU-accelerated supercomputers and design applications to optimally exploit this hardware. Since each supercomputer is typically a one-off project, the necessity of having computational languages portable across diverse CPU and GPU architectures without performance losses is increasingly compelling. Here, we study the performance portability of the LSQR algorithm as found in the AVU-GSR code of the ESA Gaia mission. This code computes the astrometric parameters of the ~108 stars in our Galaxy. The LSQR algorithm is widely used across a broad range of high-performance computing (HPC) applications, elevating the study's relevance beyond the astrophysical domain. We developed different GPU-accelerated ports based on CUDA, C++ PSTL, SYCL, OpenMP, and HIP. We carefully verified the correctness of each port and tuned them to five different GPU-accelerated platforms from NVIDIA and AMD to evaluate the performance portability (PP) in terms of the harmonic mean of the application's performance efficiency across the tested hardware. HIP was demonstrated to be the most portable solution with a 0.94 average PP across the tested problem sizes, closely followed by SYCL coupled with AdaptiveCpp (ACPP) with 0.93. If we only consider NVIDIA platforms, CUDA would be the winner with 0.97. The tuning-oblivious C++ PSTL achieves 0.62 when coupled with vendor-specific compilers.},
	author = {Malenza, Giulio and Cesare, Valentina and Santimaria, Marco Edoardo and Birke, Robert and Vecchiato, Alberto and Becciani, Ugo and Aldinucci, Marco},
	booktitle = {SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis},
	date-modified = {2025-05-24 09:39:12 +0200},
	doi = {10.1109/SCW63240.2024.00157},
	isbn = {979-8-3503-5554-3},
	keywords = {astro, icsc, eupex},
	pages = {1152--1163},
	publisher = {IEEE},
	title = {Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study},
	url = {https://conferences.computer.org/sc-wpub/pdfs/SC-W2024-6oZmigAQfgJ1GhPL0yE3pS/555400b152/555400b152.pdf},
	year = {2024},
	bdsk-url-1 = {https://conferences.computer.org/sc-wpub/pdfs/SC-W2024-6oZmigAQfgJ1GhPL0yE3pS/555400b152/555400b152.pdf}}

@inproceedings{Antelmi_ISWCWrks_24,
	abstract = {The growing availability of (linked) open data requires lay users to master how to deal with data effectively, yet SPARQL presents a barrier to leveraging data represented as knowledge graphs. As the block programming paradigm has been successfully used to teach programming skills, we demonstrate how to use KGSnap!, an extension of the block-based programming environment Snap!, to foster knowledge graph literacy among individuals lacking expertise in query languages. This work mainly focuses on the visualization and interaction aspects of KGSnap!, a visual SPARQL query builder, when experienced by users without expertise in the Semantic Web technologies. The reported experience is discussed as a learning-by-doing protocol aimed at facilitating the reproducibility and transparency of the performed evaluation. KGSnap! ease of use has been verified by 14 Snap! experts and 24 high-school learners. The findings indicate that lay users perceived it as a promising approach to acquaint themselves with knowledge graphs.},
	author = {Antelmi, Alessia and Offertucci, Vincenzo and Pellegrino, Maria Angela},
	booktitle = {Proceedings of the 9th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and Knowledge Graphs co-located with the 23rd International Semantic Web Conference (ISWC 2024)},
	keywords = {analytics},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {KGSnap! in Practice: a Block-based Programming Environment for Enabling Knowledge Graph Literacy},
	url = {https://ceur-ws.org/Vol-3773/paper6.pdf},
	volume = {3773},
	year = {2024},
	bdsk-url-1 = {https://ceur-ws.org/Vol-3773/paper6.pdf}}

@inproceedings{Antelmi_BigHPC_24,
	abstract = {An agent-based model (ABM) is a computational model for simulating autonomous agents' actions and interactions to understand a system's behavior and what governs its outcomes. When the data or number of agents grow or multiple runs are necessary, agent-based simulations are generally computationally costly. Therefore, adopting different computing paradigms, such as the distributed one, is essential to manage long-running simulations. The main problem with this approach is finding a way to distribute and balance the simulation field so that the agents can move from one machine to another with the least amount of synchronization overhead. Based on our experiences, we present a Rust-based ABM engine capable of distributing models on high-performance computing resources, gaining remarkable speedup against the sequential version.},
	author = {De Vinco, Daniele and Tranquillo, Andrea and Antelmi, Alessia and Spagnuolo, Carmine and Scarano, Vittorio},
	booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3\textsuperscript{rd} Italian Conference on Big Data and Data Science, ITADATA2024},
	editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},
	keywords = {icsc, analytics},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {High-Performance Computation on a Rust-based distributed ABM engine},
	url = {https://ceur-ws.org/Vol-3785/paper124.pdf},
	volume = {3785},
	year = {2024},
	bdsk-url-1 = {https://ceur-ws.org/Vol-3785/paper124.pdf}}

@inproceedings{harrak2024fedsurvboost,
	abstract = {This work proposes FedSurvBoost, a federated learning pipeline for survival analysis based on the AdaBoost.F algorithm, which iteratively aggregates the best local weak hypotheses. Our method extends AdaBoost.F by removing the dependence on the number of classes coefficient from the computation of the weights of the best model. This makes it suitable for regression tasks, such as survival analysis. We show the effectiveness of our approach by comparing it with state-of-the-art methods, specifically developed for survival analysis problems, on two common survival datasets. Our code is available at https://github.com/oussamaHarrak/FedSurvBoost.},
	author = {Harrak, Oussama and Casella, Bruno and Fonio, Samuele and Fariselli, Piero and Mittone, Gianluca and Sanavia, Tiziana and Aldinucci, Marco},
	booktitle = {Proceedings of the ECML-PKDD Workshop, 2nd workshop on advancements in Federated Learning},
	keywords = {epi, icsc, fl, ai},
	location = {Vilnius, Lithuania},
	title = {{Federated AdaBoost for Survival Analysis}},
	url = {https://iris.unito.it/retrieve/9d7ca2cc-ce81-4784-90cf-c5b5cf9739fb/WAFL___ECMLPKDD___FedSurvBoost.pdf},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/9d7ca2cc-ce81-4784-90cf-c5b5cf9739fb/WAFL___ECMLPKDD___FedSurvBoost.pdf}}

@inproceedings{24:garcia:itadata,
	abstract = {The rising usage of compute-intensive AI applications with fast response time requirements, such as text generation using large language models, underscores the need for more efficient and versatile hardware solutions. This drives the exploration of emerging architectures like RISC-V, which has the potential to deliver strong performance within tight power constraints. The recent commercial release of processors with RISC-V Vector (RVV) silicon-enabled extensions further amplifies the significance of RISC-V architectures, offering enhanced capabilities for parallel processing and accelerating tasks critical to large language models and other AI applications. This work aims to evaluate the BERT and GPT-2 language models inference performance on the SOPHON SG2042 64-core RISC-V architecture with silicon-enabled RVV v0.7.1. We benchmarked the models with and without RVV, using OpenBLAS and BLIS as BLAS backends for PyTorch to enable vectorization. Enabling RVV in OpenBLAS improved the inference performance by up to 40\% in some cases.},
	author = {Garcia, Adriano Marques and Malenza, Giulio and Birke, Robert and Aldinucci, Marco},
	booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3\textsuperscript{rd} Italian Conference on Big Data and Data Science, ITADATA2024},
	editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},
	keywords = {eupilot, icsc},
	location = {Pisa, Italy},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {{Assessing Large Language Models Inference Performance on a 64-core RISC-V CPU with Silicon-Enabled Vectors}},
	url = {https://iris.unito.it/retrieve/1540f675-5e88-4f57-95e7-df8e0fe5f1df/paper110.pdf},
	volume = {3785},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/1540f675-5e88-4f57-95e7-df8e0fe5f1df/paper110.pdf}}

@inproceedings{24:brescia:itadata,
	abstract = {Data confidentiality is a critical issue in the digital age, impacting interactions between users and public services and between scientific computing organizations and Cloud and HPC providers. Performance in parallel computing is essential, yet techniques for establishing Trusted Execution Environments (TEEs) to ensure privacy in remote environments often negatively impact execution time. This paper aims to analyze the performance of a parallel bioinformatics workload for DNA alignment (Bowtie2) executed within the confidential enclaves of Intel SGX processors. The results provide encouraging insights regarding the feasibility of using SGX-based TEEs for parallel computing on large datasets. The findings indicate that, under conditions of high parallelization and with twice as many threads, workloads executed within SGX enclaves perform, on average, 15\% faster than non-confidential execution. This empirical demonstration supports the potential of SGX-based TEEs to effectively balance the need for privacy with the demands of high-performance computing.},
	author = {Brescia, Lorenzo and Colonnelli, Iacopo and Aldinucci, Marco},
	booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3\textsuperscript{rd} Italian Conference on Big Data and Data Science, ITADATA2024},
	editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},
	keywords = {icsc, confidential},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {Performance Analysis on DNA Alignment Workload with Intel SGX Multithreading},
	url = {https://ceur-ws.org/Vol-3785/paper107.pdf},
	volume = {3785},
	year = {2024},
	bdsk-url-1 = {https://ceur-ws.org/Vol-3785/paper107.pdf}}

@inproceedings{24:casella:fedrec,
	abstract = {We propose FedRec, a federated learning workflow taking advantage of unlabelled data in a semi-supervised environment to assist in the training of a supervised aggregated model. In our proposed method, an encoder architecture extracting features from unlabelled data is aggregated with the feature extractor of a classification model via weight averaging. The fully connected layers of the supervised models are also averaged in a federated fashion. We show the effectiveness of our approach by comparing it with the state-of-the-art federated algorithm, an isolated and a centralised baseline, on novel cloud detection datasets.},
	author = {Casella, Bruno and Chisari, Alessio Barbaro and Aldinucci, Marco and Battiato, Sebastiano and Giuffrida, Mario Valerio},
	booktitle = {Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, {ESANN}},
	doi = {10.14428/esann/2024.es2024-214},
	keywords = {icsc, epi, ai},
	location = {Bruges, Belgium},
	month = oct,
	title = {Federated Learning in a Semi-Supervised Environment for Earth Observation Data},
	url = {https://iris.unito.it/retrieve/a798d7b8-6b98-48c2-92f4-327d2aaa8788/ES2024-214.pdf},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/a798d7b8-6b98-48c2-92f4-327d2aaa8788/ES2024-214.pdf}}

@inproceedings{24:casella:frocks,
	abstract = {This paper proposes FROCKS, a federated time series classification method using ROCKET features. Our approach dynamically adapts the models' features by selecting and exchanging the best-performing ROCKET kernels from a federation of clients. Specifically, the server gathers the best-performing kernels of the clients together with the associated model parameters, and it performs a weighted average if a kernel is best-performing for more than one client. We compare the proposed method with state-of-the-art approaches on the UCR archive binary classification datasets and show superior performance on most datasets.},
	author = {Casella, Bruno and Matthias, Jakobs and Aldinucci, Marco and Buschjager, Sebastian},
	booktitle = {Proceedings of the 32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, {ESANN}},
	doi = {10.14428/esann/2024.es2024-61},
	keywords = {icsc, epi, ai},
	location = {Bruges, Belgium},
	month = oct,
	title = {Federated Time Series Classification with ROCKET features},
	url = {https://iris.unito.it/retrieve/51b63fc1-3e22-4ad4-8926-84af69cde739/ES2024-61.pdf},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/51b63fc1-3e22-4ad4-8926-84af69cde739/ES2024-61.pdf}}

@inproceedings{Antelmi_KDD_2024,
	abstract = {Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.},
	address = {New York, NY, USA},
	author = {Kim, Sunwoo and Lee, Soo Yong and Gao, Yue and Antelmi, Alessia and Polato, Mirko and Shin, Kijung},
	booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
	doi = {10.1145/3637528.3671457},
	isbn = {9798400704901},
	keywords = {icsc, analytics, ai},
	location = {Barcelona, Spain},
	numpages = {11},
	pages = {6534--6544},
	publisher = {Association for Computing Machinery},
	series = {KDD '24},
	title = {A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide},
	url = {https://doi.org/10.1145/3637528.3671457},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1145/3637528.3671457}}

@article{24:pone:wfrunrocrate,
	abstract = {Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing representations of workflow plans and their executions as well as means of packaging the resulting information for archiving and sharing. However, existing approaches tend to lack interoperable adoption across workflow management systems. In this work we present Workflow Run RO-Crate, an extension of RO-Crate (Research Object Crate) and Schema.org to capture the provenance of the execution of computational workflows at different levels of granularity and bundle together all their associated objects (inputs, outputs, code, etc.). The model is supported by a diverse, open community that runs regular meetings, discussing development, maintenance and adoption aspects. Workflow Run RO-Crate is already implemented by several workflow management systems, allowing interoperable comparisons between workflow runs from heterogeneous systems. We describe the model, its alignment to standards such as W3C PROV, and its implementation in six workflow systems. Finally, we illustrate the application of Workflow Run RO-Crate in two use cases of machine learning in the digital image analysis domain.},
	author = {Leo, Simone and Crusoe, Michael R. and Rodr\'{i}guez-Navas, Laura and Sirvent, Ra"{u}l and Kanitz, Alexander and De Geest, Paul and Wittner, Rudolf and Pireddu, Luca and Garijo, Daniel and Fern\'{a}ndez, Jos\'{e} M. and Colonnelli, Iacopo and Gallo, Matej and Ohta, Tazro and Suetake, Hirotaka and Capella-Gutierrez, Salvador and de Wit, Renske and Kinoshita, Bruno P. and Soiland-Reyes, Stian},
	doi = {10.1371/journal.pone.0309210},
	journal = {PLoS ONE},
	keywords = {streamflow, icsc, across, eupex},
	month = sep,
	number = {9},
	pages = {1--35},
	publisher = {Public Library of Science},
	title = {Recording provenance of workflow runs with RO-Crate},
	url = {https://iris.unito.it/retrieve/57752f7b-9f8f-4013-8cef-5b498703d882/journal.pone.0309210.pdf},
	volume = {19},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/57752f7b-9f8f-4013-8cef-5b498703d882/journal.pone.0309210.pdf},
	bdsk-url-2 = {https://doi.org/10.1371/journal.pone.0309210}}

@inproceedings{24:madrid:hipes,
	abstract = {In large distributed systems, failures are a daily event occurring frequently, especially with growing numbers of computation tasks and locations on which they are deployed. The advantage of representing an application as a workflow is possibility to utilize the Workflow Management Systems which are reliable systems guaranteeing the correct execution of the application and providing the features such as portability, scalability, and fault tolerance. Over recent years, the emergence of hybrid workflows has posed new and intriguing challenges by increasing the possibility of distributing computations involving heterogeneous and independent environments. As a consequence, the number of possible points of failure in the execution augmented, creating different important challenges interesting to study.},
	address = {Madrid, Spain},
	annote = {accepted},
	author = {Alberto Mulone and Doriana Medi\'{c} and Marco Aldinucci},
	booktitle = {1st workshop about {H}igh-{P}erformance e-{S}cience (HiPES)},
	doi = {10.1007/978-3-031-90203-1_13},
	editor = {Caino-Lores, Silvina and Zeinalipour, Demetris and Doudali, Thaleia Dimitra and Singh, David E. and Garz{\'o}n, Gracia Ester Mart{\'\i}n and Sousa, Leonel and Andrade, Diego and Cucinotta, Tommaso and D'Ambrosio, Donato and Diehl, Patrick and Dolz, Manuel F. and Jukan, Admela and Montella, Raffaele and Nardelli, Matteo and Garcia-Gasulla, Marta and Neuwirth, Sarah},
	isbn = {978-3-031-90203-1},
	keywords = {streamflow, icsc, eupex},
	month = aug,
	pages = {141--153},
	publisher = {Springer Nature Switzerland},
	title = {A Fault Tolerance mechanism for Hybrid Scientific Workflows},
	volume = {15386},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1007/978-3-031-90203-1_13}}

@article{24:eurohpc:xffl,
	abstract = {In a decade, AI frontier research transitioned from the researcher's workstation to thousands of high-end hardware-accelerated compute nodes. This rapid evolution shows no signs of slowing down in the foreseeable future. While top cloud providers may be able to keep pace with this growth rate, obtaining and efficiently exploiting computing resources at that scale is a daunting challenge for universities and SMEs. This work introduces the Cross-Facility Federated Learning (XFFL) framework to bridge this compute divide, extending the opportunity to efficiently exploit multiple independent data centres for extreme-scale deep learning tasks to data scientists and domain experts. XFFL relies on hybrid workflow abstractions to decouple tasks from environment-specific technicalities, reducing complexity and enhancing reusability. In addition, Federated Learning (FL) algorithms eliminate the need to move large amounts of data between different facilities, reducing time-to-solution and preserving data privacy. The XFFL approach is empirically evaluated by training a full LLaMAv2 7B instance on two facilities of the EuroHPC JU, showing how the increased computing power completely compensates for the additional overhead introduced by two data centres.},
	address = {Bruxelles, Belgium},
	author = {Colonnelli, Iacopo and Birke, Robert and Malenza, Giulio and Mittone, Gianluca and Mulone, Alberto and Galjaard, Jeroen and Chen, Lydia Y. and Bassini, Sanzio and Scipione, Gabriella and Martinovi\v{c}, Jan and Vondr\'{a}k, Vit and Aldinucci, Marco},
	booktitle = {Proceedings of the First EuroHPC user day},
	date-modified = {2025-09-23 17:53:31 +0200},
	doi = {10.1016/j.procs.2024.07.003},
	issn = {1877-0509},
	journal = {Procedia Computer Science},
	keywords = {streamflow, space, icsc, fl, hpc},
	pages = {3--12},
	publisher = {Elsevier},
	title = {Cross-Facility Federated Learning},
	url = {https://www.sciencedirect.com/science/article/pii/S1877050924016909},
	volume = {240},
	year = {2024},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S1877050924016909},
	bdsk-url-2 = {https://doi.org/10.1016/j.procs.2024.07.003}}

@article{24:angiography:TMI,
	abstract = {The quantification of stenosis severity from X-ray catheter angiography is a challenging task. Indeed, this requires to fully understand the lesion's geometry by analyzing dynamics of the contrast material, only relying on visual observation by clinicians. To support decision making for cardiac intervention, we propose a hybrid CNN-Transformer model for the assessment of angiography-based non-invasive fractional flow-reserve (FFR) and instantaneous wave-free ratio (iFR) of intermediate coronary stenosis. Our approach predicts whether a coronary artery stenosis is hemodynamically significant and provides direct FFR and iFR estimates. This is achieved through a combination of regression and classification branches that forces the model to focus on the cut-off region of FFR (around 0.8 FFR value), which is highly critical for decision-making. We also propose a spatio-temporal factorization mechanisms that redesigns the transformer's self-attention mechanism to capture both local spatial and temporal interactions between vessel geometry, blood flow dynamics, and lesion morphology. The proposed method achieves state-of-the-art performance on a dataset of 778 exams from 389 patients. Unlike existing methods, our approach employs a single angiography view and does not require knowledge of the key frame; supervision at training time is provided by a classification loss (based on a threshold of the FFR/iFR values) and a regression loss for direct estimation. Finally, the analysis of model interpretability and calibration shows that, in spite of the complexity of angiographic imaging data, our method can robustly identify the location of the stenosis and correlate prediction uncertainty to the provided output scores.},
	author = {Mineo, Raffaele and Proietto Salanitri, Federica and Bellitto, Giovanni and Kavasidis, Isaak and De Filippo, Ovidio. and Millesimo, Michele and de Ferrari, Gaetano Maria and Aldinucci, Marco and Giordano, Daniela and Palazzo, Simone and D'Ascenzo, Fabrizio and Spampinato, Concetto},
	date-modified = {2024-08-18 15:19:41 +0200},
	doi = {10.1109/TMI.2024.3383283},
	journal = {IEEE Transaction on Medical Imaging},
	keywords = {ai, cardio},
	number = {8},
	pages = {2866--2877},
	publisher = {IEEE},
	title = {A Convolutional-Transformer Model for FFR and iFR Assessment from Coronary Angiography},
	url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582501},
	volume = {43},
	year = {2024},
	bdsk-url-1 = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10582501},
	bdsk-url-2 = {https://doi.org/10.1109/TMI.2024.3383283}}

@inproceedings{24:queyrut:srds,
	author = {Simon Queyrut and Robert Birke and Pascal Felber and Valerio Schiavon},
	booktitle = {43rd International Symposium on Reliable Distributed Systems {SRDS}},
	date-modified = {2024-07-28 00:23:59 +0200},
	keywords = {icsc, ai},
	title = {CLUES: Collusive Theft of Conditional Generative Adversarial Networks},
	year = {2024}}

@inproceedings{23:brescia:wide,
	abstract = {In scientific environments, the frequent need to process substantial volumes of data poses a common challenge. Individuals tasked with executing these computations frequently encounter a deficit in local computational resources, leading them to opt for the facilities of a Cloud Service Provider (CSP) for data processing. However, the data subjected to these calculations may be subject to confidentiality constraints. This paper introduces a proof-of-concept framework that leverages Gramine LibOS and Intel SGX, enabling the protection of generic remote workflow computations through SGX enclaves as Trusted Execution Environments (TEEs). The framework entails the delineation of user and CSP behavior and has been implemented using Bash scripts. Furthermore, an infrastructure has been designed for the Data Center Attestation Primitives (DCAP) remote attestation mechanism, wherein the user gains trust in the proper instantiation of the enclave within the CSP. To assess the framework efficacy, it has been tested on two distinct workflows, one trivial and the other involving real-world bioinformatics applications for processing DNA data. The performance study revealed that the framework incurred an acceptable overhead, ranging from a factor of x1.4 to x1.8 compared to unsafe execution practice.},
	author = {Brescia, Lorenzo and Aldinucci, Marco},
	booktitle = {{P}roc. of the 2nd {W}orkshop on {W}orkflows in {D}istributed {E}nvironments (WiDE)},
	date-modified = {2024-08-18 15:18:40 +0200},
	doi = {10.1145/3642978.3652834},
	keywords = {icsc, confidential},
	location = {Athens, Greece},
	pages = {8--13},
	publisher = {ACM},
	title = {{S}ecure {G}eneric {R}emote {W}orkflow {E}xecution with {TEE}s},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1145/3642978.3652834}}

@article{24:casella:normalization,
	author = {Casella, Bruno and Esposito, Roberto and Sciarappa, Antonio and Cavazzoni, Carlo and Aldinucci, Marco},
	date-modified = {2025-09-23 17:48:26 +0200},
	doi = {10.1109/ACCESS.2024.3383783},
	journal = {IEEE Access},
	keywords = {epi, icsc, ai},
	pages = {47961--47971},
	title = {Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios},
	volume = {12},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1109/ACCESS.2024.3383783}}

@article{24:jsupe:Gaia,
	abstract = {The computing capacity needed to process the data generated in modern scientific experiments is approaching ExaFLOPs. Currently, achieving such performances is only feasible through GPU-accelerated supercomputers. Different languages were developed to program GPUs at different levels of abstraction. Typically, the more abstract the languages, the more portable they are across different GPUs. However, the less abstract and co-designed with the hardware, the more room for code optimization and, eventually, the more performance. In the HPC context, portability and performance are a fairly traditional dichotomy. The current C++ Parallel Standard Template Library (PSTL) has the potential to go beyond this dichotomy. In this work, we analyze the main performance benefits and limitations of PSTL using as a use-case the Gaia Astrometric Verification Unit-Global Sphere Reconstruction parallel solver developed by the European Space Agency Gaia mission. The code aims to find the astrometric parameters of $$\sim10^8$$stars in the Milky Way by iteratively solving a linear system of equations with the LSQR algorithm, originally GPU-ported with the CUDA language. We show that the performance obtained with the PSTL version, which is intrinsically more portable than CUDA, is comparable to the CUDA one on NVIDIA GPU architecture.},
	author = {Malenza, Giulio and Cesare, Valentina and Aldinucci, Marco and Becciani, Ugo and Vecchiato, Alberto},
	date-modified = {2025-05-24 09:39:04 +0200},
	doi = {10.1007/s11227-024-06011-1},
	issn = {09208542},
	journal = {The Journal of Supercomputing},
	keywords = {astro, icsc, HPC, eupex},
	month = mar,
	publisher = {Springer},
	title = {Toward HPC application portability via C++ PSTL: the Gaia AVU-GSR code assessment},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1007/s11227-024-06011-1}}

@inproceedings{24:pdp:karmarkar,
	abstract = {Optimization problems are one of the main focus of scientific research. Their computational-intensive nature makes them prone to be parallelized with consistent improvements in performance. This paper sheds light on different parallel models for accelerating Karmarkar's Interior-point method. To do so, we assess parallelization strategies for individual operations within the aforementioned Karmarkar's algorithm using OpenMP, GPU acceleration with CUDA, and the recent Parallel Standard C++ Linear Algebra library (PSTL) executing both on GPU and CPU. Our different implementations yield interesting benchmark results that show the optimal approach for parallelizing interior point algorithms for general Linear Programming (LP) problems. In addition, we propose a more theoretical perspective of the parallelization of this algorithm, with a detailed study of our OpenMP implementation, showing the limits of optimizing the single operations},
	address = {New York City, NYC, USA},
	author = {Santimaria, Marco Edoardo and Fonio, Samuele and Malenza, Giulio and Colonnelli, Iacopo and Aldinucci, Marco},
	booktitle = {Proc. of 32nd Euromicro intl. Conference on Parallel, Distributed and Network-based Processing (PDP)},
	date-modified = {2024-08-18 15:22:13 +0200},
	doi = {10.1109/PDP62718.2024.00010},
	editor = {Adriana E. Chis, Horacio Gonz{\'a}lez-V{\'e}lez},
	isbn = {979-8-3503-6307-4},
	issn = {2377-5750},
	keywords = {icsc, HPC},
	location = {Dublin, Ireland},
	month = mar,
	pages = {1--8},
	publisher = {IEEE},
	title = {Benchmarking Parallelization Models through Karmarkar Interior-point method},
	url = {https://hdl.handle.net/2318/1964571},
	year = {2024},
	bdsk-url-1 = {https://hdl.handle.net/2318/1964571},
	bdsk-url-2 = {https://doi.org/10.1109/PDP62718.2024.00010}}

@inproceedings{24:betianu:llm,
	abstract = {Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several labels (classes). When encountering domain shifts, e.g., classifier of movie reviews from IMDb to Rotten Tomatoes, adapting such an LLM-based multi-label classifier is challenging due to incomplete label sets at the target domain and daunting training overhead. The existing domain adaptation methods address either image multi-label classifiers or text binary classifiers. In this paper, we design DALLMi, Domain Adaptation Large Language Model interpolator, a first-of-its-kind semi-supervised domain adaptation method for text data models based on LLMs, specifically BERT. The core of DALLMi is the novel variation loss and MixUp regularization, which jointly leverage the limited positively labeled and large quantity of unlabeled text and, importantly, their interpolation from the BERT word embeddings. DALLMi also introduces a label-balanced sampling strategy to overcome the imbalance between labeled and unlabeled data. We evaluate DALLMi against the partial-supervised and unsupervised approach on three datasets under different scenarios of label availability for the target domain. Our results show that DALLMi achieves higher mAP than unsupervised and partially-supervised approaches by 19.9\% and 52.2\%, respectively.},
	author = {Betianu, Miruna and Malan, Abele and Aldinucci, Marco and Birke, Robert and Chen, Lydia Y.},
	booktitle = {Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining},
	date-modified = {2025-05-24 10:34:46 +0200},
	doi = {10.1007/978-981-97-2259-4_21},
	editor = {De{-}Nian Yang and Xing Xie and Vincent S. Tseng and Jian Pei and Jen{-}Wei Huang and Jerry Chun{-}Wei Lin},
	keywords = {icsc, eupilot, ai},
	location = {Taipei, Taiwan},
	month = may,
	pages = {277--289},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	title = {{DALLMi}: Domain Adaption for LLM-Based Multi-label Classifier},
	url = {https://hdl.handle.net/2318/1976672},
	volume = {14647},
	year = {2024},
	bdsk-url-1 = {https://hdl.handle.net/2318/1976672},
	bdsk-url-2 = {https://doi.org/10.1007/978-981-97-2259-4%5C_21}}

@inproceedings{24:chen:llm,
	abstract = {Knowledge distillation has been applied on generative models, such as Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). To distill the knowledge, the synthetic outputs of a teacher generator are used to train a student model. While the dark knowledge, i.e., the probabilistic output, is well explored in distilling classifiers, little is known about the existence of an equivalent dark knowledge for generative models and its extractability. In this paper, we derive the first kind of empirical risk bound for distilling generative models from a Bayesian perspective. Through our analysis, we show the existence of the dark knowledge for generative models, i.e., Bayes probability distribution of a synthetic output from a given input, which achieves lower empirical risk bound than merely using the synthetic output of the generators. Furthermore, we propose a {D}ark {K}nowledge based {D}istillation , {DKtill}, which trains the student generator based on the (approximate) dark knowledge. Our extensive evaluation on distilling VAE, conditional GANs, and translation GANs on Facades and CelebA datasets show that the FID of student generators trained by {DKtill} combining dark knowledge are lower than student generators trained only by the synthetic outputs by up to 42.66\%, and 78.99\%, respectively.},
	author = {Hong, Chi and Birke, Robert and Chen, Pin-Yu and Chen, Lydia Y.},
	booktitle = {Proceedings of the 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining},
	doi = {10.1007/978-981-97-2253-2\_19},
	editor = {De{-}Nian Yang and Xing Xie and Vincent S. Tseng and Jian Pei and Jen{-}Wei Huang and Jerry Chun{-}Wei Lin},
	keywords = {icsc, epi, ai},
	location = {Taipei, Taiwan},
	month = may,
	pages = {235--247},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	title = {{On Dark Knowledge for Distilling Generators}},
	url = {https://hdl.handle.net/2318/1976671},
	volume = {14646},
	year = {2024},
	bdsk-url-1 = {https://hdl.handle.net/2318/1976671},
	bdsk-url-2 = {https://doi.org/10.1007/978-981-97-2253-2%5C_19}}

@inproceedings{24:casella:sgx,
	abstract = {Federated Learning (FL) has emerged as a solution to preserve data privacy by keeping the data locally on each participant's device. However, FL alone is still vulnerable to attacks that can cause privacy leaks. Therefore, it becomes necessary to take additional security measures at the cost of increasing runtimes. The Trusted Execution Environment (TEE) approach promises to offer the highest degree of security during execution. However, TEEs suffer from memory limits which prevent safe end-to-end FL training of modern deep models. State-of- the-art approaches limit secure training to selected layers, failing to avert the full spectrum of attacks or adopt layer-wise training affecting model performance. We benchmark the usage of a library OS (LibOS) to run the full, unmodified end-to-end FL training inside the TEE. We extensively evaluate and model the overhead of the different security mechanisms needed to protect the data and model during computation (TEE), communication (TLS), and storage (disk encryption). The obtained results across three datasets and two models demonstrate that LibOSes are a viable way to seamlessly inject security into FL with limited overhead (at most 2x), offering valuable guidance for researchers and developers aiming to apply FL in data-security-focused contexts.},
	author = {Casella, Bruno and Colonnelli, Iacopo and Mittone, Gianluca and Birke, Robert and Riviera, Walter and Sciarappa, Antonio and Cavazzoni, Carlo and Aldinucci, Marco},
	booktitle = {Proceedings of the 2024 Deep Learning Security and Privacy Workshop, IEEE Symposium on Security and Privacy 2024},
	date-modified = {2024-08-18 13:16:08 +0200},
	doi = {10.1109/SPW63631.2024.00009},
	keywords = {icsc, epi, confidential, fl},
	location = {San Francisco, CA},
	month = may,
	title = {A Performance Analysis for Confidential Federated Learning},
	url = {https://iris.unito.it/retrieve/b5877a97-2d8d-4e95-8791-0aa4a1b953b3/DLSP___CONFIDENTIAL_FL.pdf},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/b5877a97-2d8d-4e95-8791-0aa4a1b953b3/DLSP___CONFIDENTIAL_FL.pdf}}

@article{GARCIA:JSuper:24,
	abstract = {GrPPI library aims to simplify the burdening task of parallel programming. It provides a unified, abstract, and generic layer while promising minimal overhead on performance. Although it supports stream parallelism, GrPPI lacks an evaluation regarding representative performance metrics for this domain, such as throughput and latency. This work evaluates GrPPI focused on parallel stream processing. We compare the throughput and latency performance, memory usage, and programmability of GrPPI against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks and benchmarks with handwritten parallel code using the same backends supported by GrPPI. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is often competitive with handwritten parallel code, the infeasibility of fine-tuning GrPPI is a crucial drawback for emerging applications. Despite this, programmability experiments estimate that GrPPI can potentially reduce the development time of parallel applications by about three times.},
	author = {Garcia, Adriano Marques and Griebler, Dalvan and Schepke, Claudio and Garc\'{i}a, Jos\'{e} Daniel and Mu\~{n}oz, Javier Fern\'{a}ndez and Fernandes, Luiz Gustavo},
	doi = {10.1007/s11227-024-05934-z},
	isbn = {1573-0484},
	journal = {The Journal of Supercomputing},
	keywords = {admire},
	number = {In press},
	pages = {1--35},
	publisher = {Springer},
	title = {Performance and programmability of GrPPI for parallel stream processing on multi-cores},
	url = {https://iris.unito.it/retrieve/fff66640-fcbe-4080-a4f1-3279c9fadafb/s11227-024-05934-z.pdf},
	volume = {In press},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/fff66640-fcbe-4080-a4f1-3279c9fadafb/s11227-024-05934-z.pdf}}

@inproceedings{Antelmi_WebSci_2024,
	abstract = {Understanding the structural and linguistic properties of conversational data in social media is crucial for extracting meaningful insights to understand opinion dynamics, (mis-)information spreading, and the evolution of harmful behavior. Current state-of-the-art mathematical frameworks, such as hypergraphs and linguistic tools, such as large language models (LLMs), offer robust methodologies for modeling high-order group interactions and unprecedented capabilities for dealing with natural language-related tasks. In this study, we propose an innovative approach that blends these worlds by abstracting conversational networks via hypergraphs and analyzing their dynamics through LLMs. Our aim is to enhance the stance detection task by incorporating the high-order interactions naturally embedded within a conversation, thereby enriching the contextual understanding of LLMs regarding the intricate human dynamics underlying social media data.},
	address = {New York, NY, USA},
	author = {De Vinco, Daniele and Antelmi, Alessia and Spagnuolo, Carmine and Aiello, Luca Maria},
	booktitle = {Companion Publication of the 16th ACM Web Science Conference},
	doi = {10.1145/3630744.3658418},
	isbn = {9798400704536},
	keywords = {icsc, analytics},
	location = {Stuttgart, Germany},
	numpages = {2},
	pages = {3--4},
	publisher = {Association for Computing Machinery},
	series = {Websci Companion '24},
	title = {Deciphering Conversational Networks: Stance Detection via Hypergraphs and LLMs},
	url = {https://doi.org/10.1145/3630744.3658418},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1145/3630744.3658418}}

@inproceedings{Antelmi_WAW_2024,
	abstract = {Hypergraph research has been thriving over the past few years, with a growing interest in a plethora of domains. Despite this remarkable surge, the lack of a comprehensive platform for searching and downloading diverse and well-curated datasets poses a significant obstacle to the continued advancement of the field. This absence hinders the ability of researchers and practitioners to validate and benchmark their hypergraph algorithms and models effectively.},
	address = {Cham},
	author = {Antelmi, Alessia and De Vinco, Daniele and Spagnuolo, Carmine},
	booktitle = {Modelling and Mining Networks},
	doi = {10.1007/978-3-031-59205-8_11},
	editor = {Dewar, Megan and Kami{\'{n}}ski, Bogumi{\l} and Kaszy{\'{n}}ski, Daniel and Krai{\'{n}}ski, {\L}ukasz and Pra{\l}at, Pawe{\l} and Th{\'e}berge, Fran{\c{c}}ois and Wrzosek, Ma{\l}gorzata},
	isbn = {978-3-031-59205-8},
	keywords = {icsc, analytics},
	pages = {159--173},
	publisher = {Springer Nature Switzerland},
	title = {HypergraphRepository: A Community-Driven and Interactive Hypernetwork Data Collection},
	year = {2024},
	bdsk-url-1 = {https://link.springer.com/chapter/10.1007/978-3-031-59205-8_11}}

@article{Antelmi_JSUPE_2024,
	abstract = {The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers.},
	author = {Antelmi, Alessia and Torquati, Massimo and Corridori, Giacomo and Gregori, Daniele and Polzella, Francesco and Spinatelli, Gianmarco and Aldinucci, Marco},
	day = {01},
	doi = {10.1007/s11227-024-06069-x},
	issn = {1573-0484},
	journal = {The Journal of Supercomputing},
	keywords = {icsc, analytics},
	month = jul,
	number = {11},
	pages = {15799--15833},
	title = {Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework},
	url = {https://doi.org/10.1007/s11227-024-06069-x},
	volume = {80},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1007/s11227-024-06069-x}}

@article{Antelmi_JASSS_2024,
	abstract = {Agent-based models represent a primary methodology to untangle and study complex systems. Over the last decade, the need for more elaborate computing-demanding models gave rise to many frameworks and tools to run ABM simulations. Current state-of-the-art ABM tools either focus on ease of use, performance, or a trade-off between these two elements. Still, efficiency-oriented solutions (required for both large and small-scale simulations) are vulnerable to memory flaws which could invalidate the experiment results. This work aims to merge efficiency, reliability, and safeness under an innovative ABM software framework based on the Rust programming language. Our framework, krABMaga, is an open-source library that offers a high-level environment by exploiting metaprogramming and expandable visualization features. We equipped our library with a dynamic simulation monitoring system and model exploration and optimization capabilities over parallel, distributed, and cloud architectures. After having presented the overall architecture and functionalities of krABMaga, we discuss a performance comparison of our framework against the mostly adopted ABM software and the scalability potential of our simulation engine on a model calibration experiment running over an AWS EC2 virtual cluster machine. All code and examples models are available on GitHub.},
	author = {Antelmi, Alessia and Caramante, Pasquale and Cordasco, Gennaro and D'Ambrosio, Giuseppe and De Vinco, Daniele and Foglia, Francesco and Postiglione, Luca and Spagnuolo, Carmine},
	doi = {10.18564/jasss.5300},
	issn = {1460-7425},
	journal = {Journal of Artificial Societies and Social Simulation},
	keywords = {icsc, analytics},
	number = {2},
	pages = {4},
	title = {Reliable and Efficient Agent-Based Modeling and Simulation},
	url = {http://jasss.soc.surrey.ac.uk/27/2/4.html},
	volume = {27},
	year = {2024},
	bdsk-url-1 = {http://jasss.soc.surrey.ac.uk/27/2/4.html}}

@article{24:casella:starprotocol,
	abstract = {Federated learning is a cooperative learning approach that has emerged as an effective way to address privacy concerns. Here, we present a protocol for training MERGE: a federated multi-input neural network (NN) for COVID-19 prognosis. We describe steps for collecting and preprocessing datasets. We then detail the process of training a multi-input NN. This protocol can be adapted for use with datasets containing both image- and table-based input sources.},
	author = {Casella, Bruno and Riviera, Walter and Aldinucci, Marco and Menegaz, Gloria},
	date-modified = {2024-04-01 11:14:16 +0200},
	doi = {10.1016/j.xpro.2023.102812},
	institution = {Computer Science Department, University of Torino},
	journal = {STAR Protocols},
	keywords = {icsc, epi},
	note = {https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf},
	title = {Protocol for training MERGE: A federated multi-input neural network for COVID-19 prognosis},
	url = {https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf},
	year = {2024},
	bdsk-url-1 = {https://prod-shared-star-protocols.s3.amazonaws.com/protocols/3225.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.xpro.2023.102812}}

@inproceedings{23:mittone:multiview,
	abstract = {Computing is evolving rapidly to cater to the increasing demand for sophisticated services, and Cloud computing lays a solid foundation for flexible on-demand provisioning. However, as the size of applications grows, the centralised client-server approach used by Cloud computing increasingly limits the applications' scalability. To achieve ultra-scalability, cloud/edge/fog computing converges into the compute continuum, completely decentralising the infrastructure to encompass universal, pervasive resources. The compute continuum makes devising applications benefitting from this complex environment a challenging research problem. We put the opportunities the compute continuum others to the test through a real-world multi-view detection model (MvDet) implemented with the FastFL C/C++ high-performance edge inference framework. Computational performance is discussed considering many experimental scenarios, encompassing different edge computational capabilities and network bandwidths. We obtain up to 1.92x speedup in inference time over a centralised solution using the same devices.},
	address = {Taormina, Italy},
	author = {Gianluca Mittone and Giulio Malenza and Marco Aldinucci and Robert Birke},
	booktitle = {Proc. of the 16th IEEE/ACM Intl. Conference on Utility and Cloud Computing Companion {(UCC)}},
	date-modified = {2024-08-18 15:14:21 +0200},
	doi = {10.1145/3603166.3632561},
	institution = {Computer Science Department, University of Torino},
	keywords = {icsc, eupilot, fl},
	month = dec,
	note = {eupilot, icsc, fl},
	pages = {1--6},
	publisher = {{ACM}},
	title = {Distributed Edge Inference: an Experimental Study on Multiview Detection},
	url = {https://iris.unito.it/handle/2318/1950083},
	volume = {30},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/handle/2318/1950083}}

@article{23:casella:FedER,
	abstract = {In the medical field, multi-center collaborations are often sought to yield more generalizable findings by leveraging the heterogeneity of patient and clinical data. However, recent privacy regulations hinder the possibility to share data, and consequently, to come up with machine learning-based solutions that support diagnosis and prognosis. Federated learning (FL) aims at sidestepping this limitation by bringing AI-based solutions to data owners and only sharing local AI models, or parts thereof, that need then to be aggregated. However, most of the existing federated learning solutions are still at their infancy and show several shortcomings, from the lack of a reliable and effective aggregation scheme able to retain the knowledge learned locally to weak privacy preservation as real data may be reconstructed from model updates. Furthermore, the majority of these approaches, especially those dealing with medical data, relies on a centralized distributed learning strategy that poses robustness, scalability and trust issues. In this paper we present a federated and decentralized learning strategy, FedER, that, exploiting experience replay and generative adversarial concepts, effectively integrates features from local nodes, providing models able to generalize across multiple datasets while maintaining privacy. FedER is tested on two tasks --- tuberculosis and melanoma classification --- using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation. Code is available at https://github.com/perceivelab/FedER},
	author = {Pennisi, Matteo and Proietto Salanitri, Federica and Bellitto, Giovanni and Casella, Bruno and Aldinucci, Marco and Palazzo, Simone and Spampinato, Concetto},
	date-modified = {2024-04-01 11:13:27 +0200},
	doi = {10.1016/j.cviu.2023.103882},
	institution = {Computer Science Department, University of Torino},
	journal = {Computer Vision and Image Understanding},
	keywords = {ai},
	optnote = {https://www.sciencedirect.com/science/article/pii/S107731422300262X?via%3Dihub},
	pages = {103882},
	title = {FedER: Federated Learning through Experience Replay and Privacy-Preserving Data Synthesis},
	url = {https://www.sciencedirect.com/science/article/pii/S107731422300262X?via%3Dihub},
	volume = {238},
	year = {2024},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S107731422300262X?via%3Dihub},
	bdsk-url-2 = {https://doi.org/10.1016/j.cviu.2023.103882}}

@inproceedings{24:esann:fonio:fedhp,
	abstract = {This paper presents FedHP, an algorithm that amalgamates federated learning, hyperspherical geometries, and prototype learning. Federated Learning (FL) has garnered attention as a privacy-preserving method for constructing robust models across distributed datasets. Traditionally, FL involves exchanging model parameters to uphold data privacy; however, in scenarios with costly data communication, exchanging large neural net- work models becomes impractical. In such instances, prototype learning provides a feasible solution by necessitating the exchange of a few class prototypes instead of entire deep learning models. Motivated by these considerations, our approach leverages recent advancements in prototype learning, particularly the benefits offered by non-Euclidean geometries. Alongside introducing FedHP, we provide empirical evidence demonstrat- ing its comparable performance to other state-of-the-art approaches while significantly reducing communication costs.},
	address = {Bruges, Belgium},
	author = {Fonio, Samuele and Polato, Mirko and Esposito, Roberto},
	booktitle = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN)},
	keywords = {icsc, ai},
	month = oct,
	title = {FedHP: Federated Learning with Hyperspherical Prototypical Regularization},
	url = {https://www.esann.org/sites/default/files/proceedings/2024/ES2024-183.pdf},
	year = {2024},
	bdsk-url-1 = {https://www.esann.org/sites/default/files/proceedings/2024/ES2024-183.pdf},
	bdsk-url-2 = {https://doi.org/10.14428/esann/2024.ES2024-183}}

@inproceedings{23:mittone:fl,
	abstract = {Federated Learning (FL) is experiencing a substantial research interest, with many frameworks being developed to allow practitioners to build federations easily and quickly. Most of these efforts do not consider two main aspects that are key to Machine Learning (ML) software: customizability and performance. This research addresses these issues by implementing an open-source FL framework named FastFederatedLearning (FFL). FFL is implemented in C/C++, focusing on code performance, and allows the user to specify any communication graph between clients and servers involved in the federation, ensuring customizability. FFL is tested against Intel OpenFL, achieving consistent speedups over different computational platforms (x86-64, ARM-v8, RISC-V), ranging from 2.5x and 3.69x. We aim to wrap FFL with a Python interface to ease its use and implement a middleware for different communication backends to be used. We aim to build dynamic federations in which relations between clients and servers are not static, giving life to an environment where federations can be seen as long-time evolving structures and exploited as services.},
	address = {Limassol, Cyprus},
	author = {Mittone, Gianluca and Birke, Robert and Aldinucci, Marco},
	booktitle = {Euro-Par 2023: Parallel Processing Workshops},
	doi = {10.1007/978-3-031-48803-0_40},
	institution = {Computer Science Department, University of Torino},
	keywords = {eupilot, icsc, hpc, fl},
	month = apr,
	pages = {321--326},
	publisher = {{Springer}},
	title = {Efficiently Distributed Federated Learning},
	url = {https://hdl.handle.net/2318/2031766},
	volume = {14352},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1007/978-3-031-48803-0_40}}

@article{22:Sulis,
	abstract = {The growing number of next-generation applications offers a relevant opportunity for healthcare services, generating an urgent need for architectures for systems integration. Moreover, the huge amount of stored information related to events can be explored by adopting a process-oriented perspective. This paper discusses an Ambient Assisted Living healthcare architecture to manage hospital home-care services. The proposed solution relies on adopting an event manager to integrate sources ranging from personal devices to web-based applications. Data are processed on a federated cloud platform offering computing infrastructure and storage resources to improve scientific research. In a second step, a business process analysis of telehealth and telemedicine applications is considered. An initial study explored the business process flow to capture the main sequences of tasks, activities, events. This step paves the way for the integration of process mining techniques to compliance monitoring in an AAL architecture framework.},
	author = {Sulis, Emilio and Amantea, Ilaria Angela and Aldinucci, Marco and Boella, Guido and Marinello, Renata and Grosso, Marco and Platter, Paolo and Ambrosini, Serena},
	date-added = {2023-02-04 18:45:08 +0100},
	date-modified = {2024-08-18 15:17:33 +0200},
	doi = {10.1007/s12652-022-04388-6},
	journal = {Journal of Ambient Intelligence and Humanized Computing},
	keywords = {ai},
	number = {5},
	pages = {2727--2735},
	title = {An ambient assisted living architecture for hospital at home coupled with a process-oriented perspective},
	url = {https://iris.unito.it/retrieve/c7eaab0b-f78b-4af0-8c17-fa5479d776e6/jaihc2021-preprint.pdf},
	volume = {15},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/c7eaab0b-f78b-4af0-8c17-fa5479d776e6/jaihc2021-preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s12652-022-04388-6}}

@article{24:fdata:zhao,
	author = {Zhao, Zilong and Kunar, Aditya and Birke, Robert and Van der Scheer, Hiek and Chen, Lydia Y.},
	doi = {10.3389/FDATA.2023.1296508},
	journal = {Frontiers Big Data},
	keywords = {ai},
	title = {{CTAB-GAN+:} enhancing tabular data synthesis},
	url = {https://doi.org/10.3389/fdata.2023.1296508},
	volume = {6},
	year = {2024},
	bdsk-url-1 = {https://iris.unito.it/retrieve/42e86324-b7bd-4236-8458-4dfc24bbff76/fdata-06-1296508.pdf}}

@article{24:tnsm:nur,
	author = {Nur Zincir{-}Heywood and Robert Birke and Elias Bou{-}Harb and Takeru Inoue and Neeraj Kumar and Hanan Lutfiyya and Deepak Puthal and Abdallah Shami and Natalia Stakhanova},
	doi = {10.1109/TNSM.2024.3416861},
	journal = {{IEEE} Trans. Netw. Serv. Manag.},
	keywords = {ai},
	number = {3},
	pages = {2608--2612},
	title = {Guest Editorial: Special section on Networks, Systems, and Services Operations and Management Through Intelligence},
	url = {https://doi.org/10.1109/TNSM.2024.3416861},
	volume = {21},
	year = {2024},
	bdsk-url-1 = {https://ieeexplore.ieee.org/document/10599564/}}

@misc{DBLP:data/11/ColonnelliMMBPA24,
  author       = {Iacopo Colonnelli and
                  Doriana Medic and
                  Alberto Mulone and
                  Viviana Bono and
                  Luca Padovani and
                  Marco Aldinucci},
  title        = {Artifact for paper "Introducing {SWIRL:} An Intermediate Representation
                  Language for Scientific Workflows" (Version 1)},
  publisher    = {Zenodo},
  year         = {2024},
  month        = jun,
  howpublished = {\url{https://doi.org/10.5281/zenodo.12523000}},
  note         = {Accessed on YYYY-MM-DD.},
  url          = {https://doi.org/10.5281/zenodo.12523000},
  doi          = {10.5281/ZENODO.12523000},
  timestamp    = {Thu, 26 Jun 2025 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/data/11/ColonnelliMMBPA24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/jlap/MalettoR24,
  author       = {Giacomo Maletto and
                  Luca Roversi},
  title        = {Certifying expressive power and algorithms of reversible primitive
                  permutations with Lean},
  journal      = {J. Log. Algebraic Methods Program.},
  volume       = {136},
  pages        = {100923},
  year         = {2024},
  url          = {https://doi.org/10.1016/j.jlamp.2023.100923},
  doi          = {10.1016/J.JLAMP.2023.100923},
  timestamp    = {Fri, 08 Mar 2024 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/jlap/MalettoR24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ictcs/PalazzoPRZ24,
  author       = {Matteo Palazzo and
                  Luca Paolini and
                  Luca Roversi and
                  Margherita Zorzi},
  editor       = {Ugo de'Liguoro and
                  Matteo Palazzo and
                  Luca Roversi},
  title        = {Host-Core Calculi for Non-classical Computations: {A} First Insight},
  booktitle    = {Proceedings of the 25th Italian Conference on Theoretical Computer
                  Science, Torino, Italy, September 11-13, 2024},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3811},
  pages        = {255--268},
  publisher    = {CEUR-WS.org},
  year         = {2024},
  url          = {https://ceur-ws.org/Vol-3811/paper280.pdf},
  timestamp    = {Thu, 07 Nov 2024 16:44:43 +0100},
  biburl       = {https://dblp.org/rec/conf/ictcs/PalazzoPRZ24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/rc/PalazzoR24,
  author       = {Matteo Palazzo and
                  Luca Roversi},
  editor       = {Torben {\AE}gidius Mogensen and
                  Lukasz Mikulski},
  title        = {Algorithmically Expressive, Always-Terminating Model for Reversible
                  Computation},
  booktitle    = {Reversible Computation - 16th International Conference, {RC} 2024,
                  Toru{\'{n}}, Poland, July 4-5, 2024, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {14680},
  pages        = {31--49},
  publisher    = {Springer},
  year         = {2024},
  url          = {https://doi.org/10.1007/978-3-031-62076-8\_3},
  doi          = {10.1007/978-3-031-62076-8\_3},
  timestamp    = {Thu, 04 Jul 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/rc/PalazzoR24.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/ictcs/2024,
  editor       = {Ugo de'Liguoro and
                  Matteo Palazzo and
                  Luca Roversi},
  title        = {Proceedings of the 25th Italian Conference on Theoretical Computer
                  Science, Torino, Italy, September 11-13, 2024},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3811},
  publisher    = {CEUR-WS.org},
  year         = {2024},
  url          = {https://ceur-ws.org/Vol-3811},
  urn          = {urn:nbn:de:0074-3811-9},
  timestamp    = {Thu, 07 Nov 2024 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/ictcs/2024.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2409-16928,
  author       = {Mario Bifulco and
                  Luca Roversi},
  title        = {Quantum-Classical Sentiment Analysis},
  journal      = {CoRR},
  volume       = {abs/2409.16928},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2409.16928},
  doi          = {10.48550/ARXIV.2409.16928},
  eprinttype   = {arXiv},
  eprint       = {2409.16928},
  timestamp    = {Thu, 17 Oct 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2409-16928.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@proceedings{DBLP:conf/itadata/2024bighpc,
  editor       = {Alessia Antelmi and
                  Emanuele Carlini and
                  Patrizio Dazzi},
  title        = {Proceedings of the 2nd Special Track on Big Data and High-Performance
                  Computing (BigHPC 2024) co-located with the 3rd Italian Conference
                  on Big Data and Data Science {(ITADATA} 2024), Pisa, Italy, September
                  17-19, 2024},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3785},
  publisher    = {CEUR-WS.org},
  year         = {2024},
  url          = {https://ceur-ws.org/Vol-3785},
  urn          = {urn:nbn:de:0074-3785-4},
  timestamp    = {Mon, 08 Dec 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/itadata/2024bighpc.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2407-01279,
  author       = {Alessia Antelmi and
                  Lucio La Cava and
                  Arianna Pera},
  title        = {Finding Hidden Swing Voters in the 2022 Italian Elections Twitter
                  Discourse},
  journal      = {CoRR},
  volume       = {abs/2407.01279},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2407.01279},
  doi          = {10.48550/ARXIV.2407.01279},
  eprinttype   = {arXiv},
  eprint       = {2407.01279},
  timestamp    = {Sat, 24 Aug 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2407-01279.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@phdthesis{phd:mittone:2024,
	title        = {Pushing Federated Learning Boundaries: Three Innovative Distributed Intelligence Approaches},
	author       = {Gianluca Mittone},
	year         = 2024,
	month        = nov,
	url          = {https://hdl.handle.net/2318/2031404},
	abstract     = {The pervasivity of artificial intelligence is structurally changing how societies envision their development. The perception of what is valuable is rapidly shifting, and data is in the eye of the storm. This process affects every level of organisation in the modern world: from single individuals to multinational companies, from public administration to international organisations, everybody is developing the consciousness that data is a new, powerful social and economic device. Data is precious, and the conflict between who tries to harvest it and who instead tries to keep it private and protect its owners’ rights rages. This friction constitutes the natural environment for developing new ideas and concepts to mitigate this contention. Federated learning arose in 2016 as one possi- ble solution to the abovementioned struggle. The fundamental concept of this paradigm is to eliminate the need to exchange data between entities in favour of exchanging the knowledge extracted from it. This approach bypasses the privacy limit imposed by data owners, effectively enabling large-scale cooperative machine learning model training and eliminating the need for invasive data harvesting. Starting from the real-world problem of creating a machine learning-based risk score for cardiological pathologies, the unsustainability of creating data lakes for such sen- sible tasks is described and discussed. Federated learning is identified as a solution to such an issue, and its main positive and negative aspects are investigated and analysed. Three assumptions of current-days federated learning software constitute the starting points for the main contributions of this dissertation: 1) the centralised struc- ture currently implemented by many commercial frameworks, 2) their inner workings being strictly tied to deep learning models, and 3) their assumption of being deployed on private, specialised computing infrastructures. The proposed research expands the federated learning paradigm to handle scenarios in which these three conditions do not hold. Such research problems are addressed methodologically and practically dur- ing the dissertation, and three open-source, proof-of-concept software is made freely available as tangible research results: FastFL, OpenFL-x, and xFFL. All the software discussed is experimentally tested on many computational infrastructures (cluster, cloud, and high-performance computing facilities) and different micro-architectures (x86-64, ARM-v8, RISC-V), and the obtained learning and computational performance is col- lected, presented, and discussed. FastFL is a Python and C/C++ software based on the FastFlow parallel program- ming framework. It aims to prove that a different take on federated learning systems’ structure design is possible. Different topologies and efficient communication protocols are exploited to push federations beyond the standard master- worker structure. These changes allow moving to more flexible and resilient structures, reducing the computation and communication overhead of commercial fed- erated learning software. OpenFL-x targets the machine learning models compatible with current federated learning frameworks. It shows how implementing the AdaBoost.F algorithm inside the Intel® OpenFL framework allows federated learning to exploit non-deep machine learning models. This model-agnosticism property is crucial for scenarios where deep learning is not applicable, and thus, neither are current federated learning frameworks. xFFL aims at bringing federated learning to complex, shared, distributed computing infrastructures. A cross-facility federated learning workload is deployed on many high-performance computing through the StreamFlow workflow manage- ment system, allowing seamless interaction with job queue managers and containerisation technologies. The data moving and deployment logic is handled automatically, allowing this approach to be exploited efficiently despite its complexity. Additionally, this dissertation makes a more theoretical contribution by highlighting the possibility of modelling FL processes from a higher-level perspective than the one used in current studies. Based on a RISC-pb2l formal language extension, a domain-specific language for federated learning is introduced, allowing more abstract reasoning about different federated learning topologies’ properties.},
	keywords     = {ai, fl},
	school       = {Computer Science Dept., University of Turin}
}

@article{GARCIA:JSuper:23,
	abstract = {Latency or throughput is often critical performance metrics in stream processing. Applications performance can fluctuate depending on the input stream. This unpredictability is due to the variety in data arrival frequency and size, complexity, and other factors. Researchers are constantly investigating new ways to mitigate the impact of these variations on performance with self-adaptive techniques involving elasticity or micro-batching. However, there is a lack of benchmarks capable of creating test scenarios to further evaluate these techniques. This work extends and improves the SPBench benchmarking framework to support dynamic micro-batching and data stream frequency management. We also propose a set of algorithms that generates the most commonly used frequency patterns for benchmarking stream processing in related work. It allows the creation of a wide variety of test scenarios. To validate our solution, we use SPBench to create custom benchmarks and evaluate the impact of micro-batching and data stream frequency on the performance of Intel TBB and FastFlow. These are two libraries that leverage stream parallelism for multi-core architectures. Our results demonstrated that our test cases did not benefit from micro-batches on multi-cores. For different data stream frequency configurations, TBB ensured the lowest latency, while FastFlow assured higher throughput in shorter pipelines.},
	author = {Garcia, Adriano Marques and Griebler, Dalvan and Schepke, Claudio and Fernandes, Luiz Gustavo},
	date-modified = {2025-05-24 10:57:13 +0200},
	doi = {10.1007/s11227-022-05024-y},
	isbn = {1573-0484},
	journal = {The Journal of Supercomputing},
	keywords = {parallel},
	number = {8},
	pages = {9206--9244},
	publisher = {Springer},
	title = {Micro-batch and data frequency for stream processing on multi-cores},
	url = {https://iris.unito.it/retrieve/9328dbca-98ae-4ac5-b856-57c72db4444a/s11227-022-05024-y_preprint.pdf},
	volume = {79},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/9328dbca-98ae-4ac5-b856-57c72db4444a/s11227-022-05024-y_preprint.pdf}}

@inproceedings{GARCIA:PDP:23,
	abstract = {Several solutions aim to simplify the burdening task of parallel programming. The GrPPI library is one of them. It allows users to implement parallel code for multiple backends through a unified, abstract, and generic layer while promising minimal overhead on performance. An outspread evaluation of GrPPI regarding stream parallelism with representative metrics for this domain, such as throughput and latency, was not yet done. In this work, we evaluate GrPPI focused on stream processing. We evaluate performance, memory usage, and programming effort and compare them against handwritten parallel code. For this, we use the benchmarking framework SPBench to build custom GrPPI benchmarks. The basis of the benchmarks is real applications, such as Lane Detection, Bzip2, Face Recognizer, and Ferret. Experiments show that while performance is competitive with handwritten code in some cases, in other cases, the infeasibility of fine-tuning GrPPI is a crucial drawback. Despite this, programmability experiments estimate that GrPPI has the potential to reduce by about three times the development time of parallel applications.},
	address = {Naples, Italy},
	author = {Garcia, Adriano Marques and Griebler, Dalvan and Schepke, Claudio and Santos, Andr\'{e} Sacilotto and Garc\'{i}a, Jos\'{e} Daniel and Mu\~{n}oz, Javier Fern\'{a}ndez and Fernandes, Luiz Gustavo},
	booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
	doi = {10.1109/PDP59025.2023.00033},
	keywords = {admire},
	month = mar,
	pages = {164--168},
	publisher = {IEEE},
	series = {PDP'23},
	title = {{A Latency, Throughput, and Programmability Perspective of GrPPI for Streaming on Multi-cores}},
	url = {https://iris.unito.it/retrieve/9165d2ef-7140-4645-87cc-269050341c1d/PDP_2023_SPbench_with_GrPPI.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/9165d2ef-7140-4645-87cc-269050341c1d/PDP_2023_SPbench_with_GrPPI.pdf}}

@inproceedings{Antelmi_CNA1_2023,
	abstract = {Given the evolving role of social media in political communication and the strategic use of these platforms by politicians to shape public opinion, research has commonly focused on investigating computational propaganda as a means for automated information diffusion. Focusing on a less explored yet promising line, we aim to assess political persuasion in digital contexts by introducing a computational framework that combines Natural Language Processing and Network Science methods to investigate the linkage between persuasion techniques on social media and personality traits of online political audiences. Our final goal is to enhance public awareness of political tactics and encourage critical thinking in response to the online spread of political information.},
	author = {Antelmi, Alessia and La Cava, Luca and Pera, Arianna},
	booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},
	keywords = {icsc, analytics},
	title = {Tell Me Who You Are and I Will Predict Your Vulnerability to Political Persuasion Techniques},
	url = {https://iris.unito.it/bitstream/2318/1949370/1/_CNA__23__Personality_vs_propaganda.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/bitstream/2318/1949370/1/_CNA__23__Personality_vs_propaganda.pdf}}

@inproceedings{Antelmi_CNA_2023,
	abstract = {The volume of the Italian online political discourse on social media has recently increased, but the coverage level does not compare with other Countries such as the US. Nonetheless, researchers focused on studying polarization and homophily with respect to political debates or investigating the role of populism in online engagement. In this research landscape, the analysis of political preference shifts through social media remains to be explored. We aim to bridge this gap by examining the Twitter discourse during the 2022 Italian general elections, with a specific emphasis on political "swingers". In particular, our findings indicate a stable political discourse in Italy, yet they also uncover a growing presence of political swingers willing to shift their support to significantly different factions.},
	author = {Antelmi, Alessia and La Cava, Luca and Pera, Arianna},
	booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},
	keywords = {icsc, analytics},
	title = {Finding Hidden Swingers in the 2022 Italian Elections Twitter Discourse},
	url = {https://iris.unito.it/bitstream/2318/1949354/1/_CNA__23__TweetYourMind.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/bitstream/2318/1949354/1/_CNA__23__TweetYourMind.pdf}}

@inproceedings{Antelmi_ITADATA_2023,
	abstract = {The Software Heritage (SWH) dataset serves as a vast repository for open-source code, with the ambitious goal of preserving all publicly available open-source projects. Despite being designed to effectively archive project files, its size of nearly 1 petabyte presents challenges in efficiently supporting Big Data MapReduce or AI systems. To address this disparity and enable seamless custom analytics on the SWH dataset, we present the SWH-Analytics (SWHA) architecture. This development environment quickly and transparently runs custom analytic applications on open-source software data preserved over time by SWH.},
	author = {Antelmi, Alessia and Torquati, Massimo and Gregori, Daniele and Polzella, Francesco and Spinatelli, Gianmarco and Aldinucci, Marco},
	booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science ({ITADATA} 2023), Naples, Italy, September 11-13, 2023},
	editor = {Bena, Nicola and Di Martino, Beniamino and Maratea, Antonio and Sperduti, Alessandro and Di Nardo, Emanuel and Ciaramella, Angelo and Montella, Raffaele and Ardagna, Claudio A.},
	keywords = {admire, icsc, analytics},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {The SWH-Analytics Framework},
	url = {https://ceur-ws.org/Vol-3606/paper76.pdf},
	volume = {3606},
	year = {2023},
	bdsk-url-1 = {https://ceur-ws.org/Vol-3606/paper76.pdf}}

@inproceedings{23:colonnelli:itadata,
	abstract = {This article introduces a novel hybrid workflow abstraction that injects topology awareness directly into the definition of a distributed workflow model. In particular, the article briefly discusses the advantages brought by this approach to the design and orchestration of large-scale data-oriented workflows, the current level of support from state-of-the-art workflow systems, and some future research directions.},
	author = {Iacopo Colonnelli},
	booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science ({ITADATA} 2023), Naples, Italy, September 11-13, 2023},
	editor = {Bena, Nicola and Di Martino, Beniamino and Maratea, Antonio and Sperduti, Alessandro and Di Nardo, Emanuel and Ciaramella, Angelo and Montella, Raffaele and Ardagna, Claudio A.},
	keywords = {across, eupex, icsc, streamflow, jupyter-workflow},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {Workflow Models for Heterogeneous Distributed Systems},
	url = {https://ceur-ws.org/Vol-3606/invited77.pdf},
	volume = {3606},
	year = {2023},
	bdsk-url-1 = {https://ceur-ws.org/Vol-3606/invited77.pdf}}

@inproceedings{23:casella:onchain,
	abstract = {Blockchain, the underlying technology of Bitcoin and several other cryptocurrencies, like Ethereum, produces a massive amount of open-access data that can be analyzed, providing important information about the network's activity and its respective token. The on-chain data have extensively been used as input to Machine Learning algorithms for predicting cryptocurrencies' future prices; however, there is a lack of study in predicting the future behaviour of on-chain data. This study aims to show how on-chain data can be used to detect cryptocurrency market regimes, like minimum and maximum, bear and bull market phases, and how forecasting these data can provide an optimal asset allocation for long-term investors.},
	author = {Casella, Bruno and Paletto, Lorenzo},
	booktitle = {Proceedings of the 2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-5 May 2023, Dubai},
	doi = {https://doi.org/10.1109/ICBC56567.2023.10174989},
	keywords = {icsc, epi},
	title = {Predicting Cryptocurrencies Market Phases through On-Chain Data Long-Term Forecasting},
	url = {https://iris.unito.it/bitstream/2318/1902652/1/6.%20ICBC23%20-%20PREDICTING%20BTC.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/bitstream/2318/1902652/1/6.%20ICBC23%20-%20PREDICTING%20BTC.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/ICBC56567.2023.10174989}}

@inproceedings{23:casella:architecturalfedavg,
	abstract = {Federated Learning (FL) has emerged as a promising solution to address privacy concerns by collaboratively training Deep Learning (DL) models across distributed parties. This work proposes an architecture-based aggregation strategy in Vertical FL, where parties hold data with different attributes but shared instances. Our approach leverages the identical architectural parts, i.e. neural network layers, of different models to selectively aggregate weights, which is particularly relevant when collaborating with institutions holding different types of datasets, i.e., image, text, or tabular datasets. In a scenario where two entities train DL models, such as a Convolutional Neural Network (CNN) and a Multi-Layer Perceptron (MLP), our strategy computes the average only for architecturally identical segments. This preserves data-specific features learned from demographic and clinical data. We tested our approach on two clinical datasets, i.e., the COVID-CXR dataset and the ADNI study. Results show that our method achieves comparable results with the centralized scenario, in which all the data are collected in a single data lake, and benefits from FL generalizability. In particular, compared to the non-federated models, our proposed proof-of-concept model exhibits a slight performance loss on the COVID-CXR dataset (less than 8%), but outperforms ADNI models by up to 12%. Moreover, communication costs between training rounds are minimized by exchanging only the dense layer parameters.},
	author = {Casella, Bruno and Fonio, Samuele},
	booktitle = {Proceedings of the 3rd Workshop on Distributed Machine Learning for the Intelligent Computing Continuum (DML-ICC), IEEE/ACM UCC 2023, Taormina, Italy, 4 December 2023},
	doi = {10.1145/3603166.3632559},
	keywords = {icsc, epi, ai},
	title = {Architecture-Based FedAvg for Vertical Federated Learning},
	url = {https://iris.unito.it/retrieve/173d9960-8531-419d-9bd5-5acce6694c4e/Aggregation%20Based%20VFL.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/173d9960-8531-419d-9bd5-5acce6694c4e/Aggregation%20Based%20VFL.pdf},
	bdsk-url-2 = {10.1145/3603166.3632559}}

@inproceedings{23:casella:ERGANs,
	abstract = {Federated and continual learning are training paradigms addressing data distribution shift in space and time. More specifically, federated learning tackles non-i.i.d data in space as information is distributed in multiple nodes, while continual learning faces with temporal aspect of training as it deals with continuous streams of data. Distribution shifts over space and time is what it happens in real federated learning scenarios that show multiple challenges. First, the federated model needs to learn sequentially while retaining knowledge from the past training rounds. Second, the model has also to deal with concept drift from the distributed data distributions. To address these complexities, we attempt to combine continual and federated learning strategies by proposing a solution inspired by experience replay and generative adversarial concepts for supporting decentralized distributed training. In particular, our approach relies on using limited memory buffers of synthetic privacy-preserving samples and interleaving training on local data and on buffer data. By translating the CL formulation into the task of integrating distributed knowledge with local knowledge, our method enables models to effectively integrate learned representation from local nodes, providing models the capability to generalize across multiple datasets.We test our integrated strategy on two realistic medical image analysis tasks --- tuberculosis and melanoma classification --- using multiple datasets in order to simulate realistic non-i.i.d. medical data scenarios. Results show that our approach achieves performance comparable to standard (non-federated) learning and significantly outperforms state-of-the-art federated methods in their centralized (thus, more favourable) formulation.},
	author = {Pennisi, Matteo and Proietto Salanitri, Federica and Bellitto, Giovanni and Casella, Bruno and Aldinucci, Marco and Palazzo, Simone and Spampinato, Concetto},
	booktitle = {Proceedings of the 1st Workshop on Visual Continual Learning, ICCV 2023, Paris, France, 2 October 2023},
	date-modified = {2024-04-01 11:13:18 +0200},
	doi = {10.1109/ICCVW60793.2023.00362},
	keywords = {ai},
	note = {https://ieeexplore.ieee.org/document/10350429},
	title = {Experience Replay as an Effective Strategy for Optimizing Decentralized Federated Learning},
	url = {https://openaccess.thecvf.com/content/ICCV2023W/VCL/papers/Pennisi_Experience_Replay_as_an_Effective_Strategy_for_Optimizing_Decentralized_Federated_ICCVW_2023_paper.pdf},
	year = {2023},
	bdsk-url-1 = {https://openaccess.thecvf.com/content/ICCV2023W/VCL/papers/Pennisi_Experience_Replay_as_an_Effective_Strategy_for_Optimizing_Decentralized_Federated_ICCVW_2023_paper.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/ICCVW60793.2023.00362}}

@inproceedings{23:cloud:gdts,
	abstract = {Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data. While learning image GANs on Federated Learning (FL) and Multi-Discriminator (MD) systems has just been demonstrated, it is unknown if tabular GANs can be learned from decentralized data sources. Different from image GANs, state-of-the-art tabular GANs require prior knowledge on the data distribution of each (discrete and continuous) column to agree on a common encoding -- risking privacy guarantees. In this paper, we propose GDTS, a distributed framework for GAN-based tabular synthesizer. GDTS provides different system architectures to match the two training paradigms termed GDTS FL and GDTS MD. Key to enable learning on distributed data is the proposed novel privacy-preserving multi-source feature encoding to capture the global data properties. In addition GDTS encompasses a weighting strategy based on table similarity to counter the detrimental effects of non-IID data and a validation pipeline to easily assess and compare the performance of different paradigms and hyper parameters. We evaluate the effectiveness of GDTS in terms of synthetic data quality, and overall training scalability. Experiments show that GDTS FL achieves better statistical similarity and machine learning utility between generated and original data compared to GDTS MD.},
	address = {Chicago, USA},
	author = {Zhao, Zilong and Birke, Robert and Chen, Lydia Y.},
	booktitle = {16th {IEEE} International Conference on Cloud Computing ({CLOUD})},
	doi = {10.1109/CLOUD60044.2023.00078},
	keywords = {, ai},
	month = jul,
	publisher = {{IEEE}},
	title = {{GDTS: GAN-based Distributed Tabular Synthesizer}},
	url = {https://iris.unito.it/retrieve/8bc610de-3ccd-4a0a-b97f-ee329e487b76/GDTS_IEEE_CLOUD_preprint.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/8bc610de-3ccd-4a0a-b97f-ee329e487b76/GDTS_IEEE_CLOUD_preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/CLOUD60044.2023.00078}}

@inproceedings{23:hipc:capio,
	abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to quickly expand, further exacerbating the performance gap between computing, memory, and storage technologies. This paper introduces CAPIO (Cross-Application Programmable I/O), a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. The contribution is twofold: 1) at design time, a new I/O coordination language allows users to annotate workflow data dependencies with synchronization semantics; 2) at run time, a user-space middleware automatically and transparently to the user turns a workflow batch execution into a streaming execution according to the semantics expressed in the configuration file. CAPIO has been tested on synthetic benchmarks simulating typical workflow I/O patterns and two real-world workflows. Experiments show that CAPIO reduces the execution time by 10\% to 66\% for data-intensive workflows that use the file system as a communication medium.},
	address = {Goa, India},
	author = {Martinelli, Alberto R. and Torquati, Massimo and Aldinucci, Marco and Colonnelli, Iacopo and Cantalupo, Barbara},
	booktitle = {2023 IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)},
	doi = {10.1109/HiPC58850.2023.00031},
	keywords = {admire, eupex, icsc, capio},
	month = dec,
	publisher = {{IEEE}},
	title = {{CAPIO}: a Middleware for Transparent I/O Streaming in Data-Intensive Workflows},
	url = {https://iris.unito.it/retrieve/27380f37-0978-409e-a9d8-2b5e95a4bb85/CAPIO-HiPC23-preprint.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/27380f37-0978-409e-a9d8-2b5e95a4bb85/CAPIO-HiPC23-preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/HiPC58850.2023.00031}}

@inproceedings{23:acsos:fcppmpi,
	author = {Audrito, Giorgio and Martinelli, Alberto R. and Torta, Gianluca},
	booktitle = {2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)},
	doi = {10.1109/ACSOS-C58168.2023.00054},
	keywords = {HPC},
	pages = {140--145},
	title = {Parallelising an Aggregate Programming Framework with Message-Passing Interface},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1109/ACSOS-C58168.2023.00054}}

@article{21:mittone:coralys,
	abstract = {The present study aimed to identify patients at a higher risk of hospitalization for heart failure (HF) in a population of patients with acute coronary syndrome (ACS) treated with percutaneous coronary revascularization without a history of HF or reduced left ventricular (LV) ejection fraction before the index admission. We performed a Cox regression multivariable analysis with competitive risk and machine learning models on the incideNce and predictOrs of heaRt fAiLure After Acute coronarY Syndrome (CORALYS) registry (NCT 04895176), an international and multicenter study including consecutive patients admitted for ACS in 16 European Centers from 2015 to 2020. Of 14,699 patients, 593 (4.0%) were admitted for the development of HF up to 1 year after the index ACS presentation. A total of 2 different data sets were randomly created, 1 for the derivative cohort including 11,626 patients (80%) and 1 for the validation cohort including 3,073 patients (20%). On the Cox regression multivariable analysis, several variables were associated with the risk of HF hospitalization, with reduced renal function, complete revascularization, and LV ejection fraction as the most relevant ones. The area under the curve at 1 year was 0.75 (0.72 to 0.78) in the derivative cohort, whereas on validation, it was 0.72 (0.67 to 0.77). The machine learning analysis showed a slightly inferior performance. In conclusion, in a large cohort of patients with ACS without a history of HF or LV dysfunction before the index event, the CORALYS HF score identified patients at a higher risk of hospitalization for HF using variables easily accessible at discharge. Further approaches to tackle HF development in this high-risk subset of patients are needed.},
	author = {D'Ascenzo, Fabrizio and Fabris, Enrico and Gregorio, Caterina and Mittone, Gianluca and De Filippo, Ovidio and Wa{\'n}ha, Wojciech and Leonardi, Sergio and Raposeiras Roubin, Sergio and Chinaglia, Alessandra and Truffa, Alessandra and Huczek, Zenon and Gaibazzi, Nicola and Ielasi, Alfonso and Cortese, Bernardo and Borin, Andrea and Pagliaro, Beniamino and N{\'u}{\~n}ez-Gil, Iv{\'a}n J. and Ugo, Fabrizio and Marengo, Giorgio and Barbieri, Lucia and Marchini, Federico and Desperak, Piotr and Melendo-Viu, Mar{\'\i}a and Montalto, Claudio and Bianco, Matteo and Bruno, Francesco and Mancone, Massimo and Ferrandez-Escarabajal, Marcos and Morici, Nuccia and Scaglione, Marco and Tuttolomondo, Domenico and G{\k a}sior, Mariusz and Mazurek, Maciej and Gallone, Guglielmo and Campo, Gianluca and Wojakowski, Wojciech and Abu Assi, Emad and Stefanini, Giulio and Sinagra, Gianfranco and de Ferrari, Gaetano Maria},
	doi = {10.1016/j.amjcard.2023.08.010},
	issn = {0002-9149},
	journal = {The American Journal of Cardiology},
	keywords = {cardio, ai},
	pages = {320--329},
	title = {Forecasting the Risk of Heart Failure Hospitalization After Acute Coronary Syndromes: the CORALYS HF Score},
	url = {https://www.sciencedirect.com/science/article/pii/S000291492300766X},
	volume = {206},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1016/j.amjcard.2023.08.010},
	bdsk-url-2 = {https://hdl.handle.net/2318/1942434}}

@article{23:fl:patterns,
	abstract = {Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic images. Imaging, however, is not the only source of information. Tabular data, such as personal and genomic data and blood test results, are routinely collected but rarely considered in DL pipelines. Nevertheless, DL requires large datasets that often must be pooled from different institutions, raising non-trivial privacy concerns. Federated learning (FL) is a cooperative learning paradigm that aims to address these issues by moving models instead of data across different institutions. Here, we present a federated multi-input architecture using images and tabular data as a methodology to enhance model performance while preserving data privacy. We evaluated it on two showcases: the prognosis of COVID-19 and patients' stratification in Alzheimer's disease, providing evidence of enhanced accuracy and F1 scores against single-input models and improved generalizability against non-federated models.},
	author = {Bruno Casella and Walter Riviera and Marco Aldinucci and Gloria Menegaz},
	date-modified = {2024-04-01 11:13:40 +0200},
	doi = {10.1016/j.patter.2023.100856},
	issn = {2666-3899},
	journal = {Patterns},
	keywords = {icsc, epi, ai},
	pages = {100856},
	title = {MERGE: A model for multi-input biomedical federated learning},
	url = {https://www.sciencedirect.com/science/article/pii/S2666389923002404},
	year = {2023},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S2666389923002404},
	bdsk-url-2 = {https://doi.org/10.1016/j.patter.2023.100856}}

@inproceedings{WORKS2023,
	abstract = {An entire ecosystem of methodologies and tools revolves around scientific workflow management. They cover crucial non-functional requirements that standard workflow models fail to target, such as interactive execution, energy efficiency, performance portability, Big Data management, and intelligent orchestration in the Computing Continuum. Characterizing and monitoring this ecosystem is crucial to develop an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, collecting and analyzing 25 tools and 10 applications from several scientific domains in the context of the ``National Research Centre for HPC, Big Data, and Quantum Computing'' (ICSC). The study aims to outline the main current research directions and determine how they address the critical needs of modern scientific applications. The findings highlight a variegated research ecosystem of tools, with a prominent interest in advanced workflow orchestration and still immature but promising efforts toward energy efficiency.},
	address = {Denver, CO, USA},
	author = {Aldinucci, Marco and Maria Baralis, Elena and Cardellini, Valeria and Colonnelli, Iacopo and Danelutto, Marco and Decherchi, Sergio and Di Modica, Giuseppe and Ferrucci, Luca and Gribaudo, Marco and Iannone, Francesco and Lapegna, Marco and Medi\'{c}, Doriana and Muscianisi, Giuseppa and Righetti, Francesca and Sciacca, Eva and Tonellotto, Nicola and Tortonesi, Mauro and Trunfio, Paolo and Vardanega, Tullio},
	booktitle = {Proceedings of the {SC} '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, {SC-W} 2023},
	doi = {10.1145/3624062.3624285},
	keywords = {streamflow, jupyter-workflow, icsc},
	month = nov,
	pages = {2065--2076},
	publisher = {{ACM}},
	title = {A Systematic Mapping Study of Italian Research on Workflows},
	url = {https://doi.org/10.1145/3624062.3624285},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1145/3624062.3624285}}

@inproceedings{10.1007/978-3-031-39698-4_46,
	abstract = {The growing demands for data processing by new data-intensive applications are putting pressure on the performance and capacity of HPC storage systems. The advancement in storage technologies, such as NVMe and persistent memory, are aimed at meeting these demands. However, relying solely on ultra-fast storage devices is not cost-effective, leading to the need for multi-tier storage hierarchies to move data based on its usage. To address this issue, ad-hoc file systems have been proposed as a solution. They utilise the available storage of compute nodes, such as memory and persistent storage, to create a temporary file system that adapts to the application behaviour in the HPC environment. This work presents the design, implementation, and evaluation of a distributed ad-hoc in-memory storage system (Hercules), highlighting the new communication model included in Hercules. This communication model takes advantage of the Unified Communication X framework (UCX). This solution leverages the capabilities of RDMA protocols, including Infiniband, Onmipath, shared memory, and zero-copy transfers. The preliminary evaluation results show excellent network utilisation compared with other existing technologies.},
	address = {Cham},
	author = {Garcia-Blas, Javier and Sanchez-Gallegos, Genaro and Petre, Cosmin and Martinelli, Alberto R. and Aldinucci, Marco and Carretero, Jesus},
	booktitle = {Euro-Par 2023: Parallel Processing},
	date-modified = {2024-04-01 11:17:14 +0200},
	editor = {Cano, Jos{\'e} and Dikaiakos, Marios D. and Papadopoulos, George A. and Peric{\`a}s, Miquel and Sakellariou, Rizos},
	isbn = {978-3-031-39698-4},
	keywords = {admire, hpc},
	pages = {679--693},
	publisher = {Springer Nature Switzerland},
	title = {Hercules: Scalable and Network Portable In-Memory Ad-Hoc File System for Data-Centric and High-Performance Applications},
	year = {2023}}

@inproceedings{23:admire:cf,
	abstract = {The main objective of the ADMIRE project1 is the creation of an active I/O stack that dynamically adjusts computation and storage requirements through intelligent global coordination, the elasticity of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy, while offering quality-of-service (QoS), energy efficiency, and resilience for accessing extremely large data sets in very heterogeneous computing and storage environments. We have developed a framework prototype that is able to dynamically adjust computation and storage requirements through intelligent global coordination, separated control, and data paths, the malleability of computation and I/O, the scheduling of storage resources along all levels of the storage hierarchy, and scalable monitoring techniques. The leading idea in ADMIRE is to co-design applications with ad-hoc storage systems that can be deployed with the application and adapt their computing and I/O behaviour on runtime, using malleability techniques, to increase the performance of applications and the throughput of the applications.},
	address = {Bologna, Italy},
	author = {Carretero, Jesus and Garcia-Blas, Javier and Aldinucci, Marco and Besnard, Jean Baptiste and Acquaviva, Jean-Thomas and Brinkmann, Andr{\'e} and Vef, Marc-Andr{\'e} and Jeannot, Emmanuel and Miranda, Alberto and Nou, Ramon and Riedel, Morris and Torquati, Massimo and Wolf, Felix},
	booktitle = {20th {ACM} International Conference on Computing Frontiers ({CF} '23)},
	date-added = {2023-03-14 15:34:00 +0000},
	doi = {10.1145/3587135.3592174},
	keywords = {admire},
	month = may,
	publisher = {{ACM}},
	title = {Adaptive multi-tier intelligent data manager for Exascale},
	url = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592174},
	year = {2023},
	bdsk-url-1 = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592174},
	bdsk-url-2 = {https://doi.org/10.1145/3587135.3592174}}

@inproceedings{23:zhao:fctgan,
	abstract = {An alternative method for sharing knowledge while complying with strict data access regulations, such as the European General Data Protection Regulation (GDPR), is the emergence of synthetic tabular data. Mainstream table synthesizers utilize methodologies derived from Generative Adversarial Networks (GAN). Although several state-of-the-art (SOTA) tabular GAN algorithms inherit Convolutional Neural Network (CNN)-based architectures, which have proven effective for images, they tend to overlook two critical properties of tabular data: (i) the global correlation across columns, and (ii) the semantic invariance to the column order. Permuting columns in a table does not alter the semantic meaning of the data, but features extracted by CNNs can change significantly due to their limited convolution filter kernel size. To address the above problems, we propose FCT-GAN-- the first conditional tabular GAN to adopt Fourier networks into table synthesis. FCT-GAN enhances permutation invariant GAN training by strengthening the learning of global correlations via Fourier layers. Extensive evaluation on benchmarks and real-world datasets show that FCT-GAN can synthesize tabular data with better (up to 27.8%) machine learning utility (i.e. a proxy of global correlations) and higher (up to 26.5%) statistical similarity to real data. FCT-GAN also has the least variation on synthetic data quality among 7 SOTA baselines on 3 different training-data column orders.},
	address = {Birmingham, United Kingdom},
	author = {Zhao, Zilong and Birke, Robert and Chen, Lydia Y.},
	booktitle = {32nd {ACM} International Conference on Information and Knowledge Management ({CIKM '23})},
	doi = {10.1145/3583780.3615202},
	keywords = {icsc},
	month = oct,
	publisher = {{ACM}},
	title = {{FCT-GAN: Enhancing Global Correlation of Table Synthesis via Fourier Transform}},
	url = {https://iris.unito.it/retrieve/966ba767-dbbd-41e1-b4e3-7ab7ba09303f/FCT-GAN.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/966ba767-dbbd-41e1-b4e3-7ab7ba09303f/FCT-GAN.pdf},
	bdsk-url-2 = {https://doi.org/10.1145/3583780.3615202}}

@inproceedings{23:risc-v-summit,
	abstract = {RISC-V is an emerging instruction set architecture. Its modular and extensible open-source royalty-free design is increasingly attracting interest from both research and industry. Nowadays, different RISC-V-based boards can be bought off the shelf. However, software availability is equivalently vital in guaranteeing the RISC-V ecosystem's success. Here we contribute with the first publicly available port of PyTorch. PyTorch is one of the most popular Deep Learning libraries available today. As such, it is a crucial enabler in running state-of-the-art AI applications on RISC-V-based systems and a first step towards a fully democratic end-to-end codesign process.},
	address = {Barcelona, Spain},
	author = {Iacopo Colonnelli and Robert Birke and Marco Aldinucci},
	booktitle = {{RISC-V Summit Europe 2023}},
	keywords = {eupilot, icsc, riscv},
	month = jun,
	note = {Poster},
	title = {Experimenting with {PyTorch} on {RISC-V}},
	url = {https://iris.unito.it/retrieve/429bf344-9090-42c3-809c-1b8ac320a930/2023-06-08-Iacopo-COLONNELLI-abstract.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/429bf344-9090-42c3-809c-1b8ac320a930/2023-06-08-Iacopo-COLONNELLI-abstract.pdf}}

@article{23:GAIAMPI_PASP,
	abstract = {We ported to the GPU with CUDA the Astrometric Verification Unit--Global Sphere Reconstruction (AVU--GSR) Parallel Solver developed for the ESA Gaia mission, by optimizing a previous OpenACC porting of this application.},
	author = {Valentina Cesare and Ugo Becciani and Alberto Vecchiato and Lattanzi, Mario Gilberto and Fabio Pitari and Marco Aldinucci and Beatrice Bucciarelli},
	date-modified = {2025-05-24 10:39:55 +0200},
	doi = {10.1088/1538-3873/acdf1e},
	journal = {Publications of the Astronomical Society of the Pacific},
	key = {astro, icsc, eupex},
	keywords = {HPC},
	month = aug,
	number = {1049},
	title = {The {MPI + CUDA Gaia AVU--GSR} Parallel Solver Toward Next-generation Exascale Infrastructures},
	url = {https://iopscience.iop.org/article/10.1088/1538-3873/acdf1e/pdf},
	volume = {135},
	year = {2023},
	bdsk-url-1 = {https://iopscience.iop.org/article/10.1088/1538-3873/acdf1e/pdf},
	bdsk-url-2 = {https://doi.org/10.1088/1538-3873/acdf1e}}

@inproceedings{DBLP:conf/sebd/Esposito23,
	abstract = {Federated Learning (FL) has been proposed to develop better AI systems without compromising the privacy of final users and the legitimate interests of private companies. Initially deployed by Google to predict text input on mobile devices, FL has been deployed in many other industries. Since its introduction, Federated Learning mainly exploited the inner working of neural networks and other gradient descent-based algorithms by either exchanging the weights of the model or the gradients computed during learning. While this approach has been very successful, it rules out applying FL in contexts where other models are preferred, e.g., easier to interpret or known to work better. This paper proposes to leverage distributed versions of the AdaBoost algorithm to acquire strong federated models. In contrast with previous approaches, our proposal does not put any constraint on the client-side learning models and does not rely on inner workings of the learning algorithms used in the clients. We perform a large set of experiments on ten UCI datasets, comparing the algorithms in six non-iidness settings. Results show that the approach is effective, in the case of an IID setting, results are often near to the theoretical optimum (i.e., the performances of AdaBoost on the complete dataset). In case of non-IID settings, results very much depend on the severity of the non-IIDness.},
	author = {Roberto Esposito and Mirko Polato and Marco Aldinucci},
	booktitle = {Proc. of the 31th Italian Symposium on Advanced Database Systems,{SEBD} 2023},
	date-modified = {2025-09-23 17:57:01 +0200},
	editor = {Diego Calvanese and Claudia Diamantini and Nicola Ferro and Stefano Marchesin and Gianmaria Silvello and Letizia Tanca},
	keywords = {eupilot, ai},
	pages = {439--448},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {Boosting Methods for Federated Learning},
	url = {https://ceur-ws.org/Vol-3478/paper48.pdf},
	vol = {3478},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/7c133509-b661-488c-9e3e-793f07fa9b70/paper-ceur.pdf}}

@inproceedings{DBLP:conf/ftfjp/AngeloBDF23,
	author = {Pedro {\^{A}}ngelo and Viviana Bono and Mariangiola Dezani{-}Ciancaglini and M{\'{a}}rio Florido},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/ftfjp/AngeloBDF23.bib},
	booktitle = {Proceedings of the 25th {ACM} International Workshop on Formal Techniques for Java-like Programs, FTfJP 2023, Seattle, WA, USA, 18 July 2023},
	doi = {10.1145/3605156.3606453},
	editor = {Aaron Tomb},
	keywords = {admire, icsc},
	pages = {32--38},
	publisher = {{ACM}},
	timestamp = {Mon, 17 Jul 2023 13:10:51 +0200},
	title = {Gradual Guarantee for {FJ} with lambda-Expressions},
	url = {https://doi.org/10.1145/3605156.3606453},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1145/3605156.3606453}}

@inproceedings{23:SAMOS,
	abstract = {High-PerformanceComputing(HPC)haveevolvedtobeused to perform simulations of systems where physical experimentation is pro- hibitively impractical, expensive, or dangerous. This paper provides a general overview and showcases the analysis of non-functional properties in RISC-V-based platforms for HPCs. In particular, our analyses target the evaluation of power and energy control, thermal management, and reliability assessment of promising systems, structures, and technologies devised for current and future generation of HPC machines. The main set of design methodologies and technologies developed within the activ- ities of the Future and HPC & Big Data spoke of the National Centre of HPC, Big Data and Quantum Computing project are described along with the description of the testbed for experimenting two-phase cooling approaches.},
	address = {Samos, Greece},
	author = {Fornaciari, William and Reghenzani, Federico and Terraneo, Federico and Baroffio, Davide and Metra, Cecilia and Omana, Martin and Rodriguez Condia, Josie E. and Sonza Reorda, Matteo and Birke, Robert and Colonnelli, Iacopo and Mittone, Gianluca and Aldinucci, Marco and Mencagli, Gabriele and Iannone, Francesco and Palombi, Filippo and Zummo, Giuseppe and Cesarini, Daniele and Tesser, Federico},
	booktitle = {{Embedded Computer Systems: Architectures, Modeling, and Simulation - 23rd International Conference, {SAMOS} 2023}},
	doi = {10.1007/978-3-031-46077-7_26},
	keywords = {icsc, riscv, hpc},
	note = {icsc},
	title = {{RISC-V}-based Platforms for {HPC}: Analyzing Non-functional Properties for Future {HPC} and {Big-Data} Clusters},
	url = {https://iris.unito.it/retrieve/b627eab0-3aa1-4fd7-8685-f47c62c792b3/SAMOS_2023_CN_HPC_FL1.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/b627eab0-3aa1-4fd7-8685-f47c62c792b3/SAMOS_2023_CN_HPC_FL1.pdf}}

@inproceedings{23:esann:fonio,
	abstract = {In this paper, we explore the usage of hierarchical priors to improve learning in contexts where the number of available examples is extremely low. Specifically, we consider a Prototype Learning setting where deep neural networks are used to embed data in hyperspherical geometries.In this scenario, we propose an innovative way to learn the prototypes by combining class separation and hierarchical information. In addition, we introduce a contrastive loss function capable of balancing the exploitation of prototypes through a prototype pruning mechanism. We compare the proposed method with state-of-the-art approaches on two public datasets.},
	address = {Bruges, Belgium},
	author = {Fonio, Samuele and Paletto, Lorenzo and Cerrato, Mattia and Ienco, Dino and Esposito, Roberto},
	booktitle = {31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, {ESANN}},
	keywords = {icsc, ai},
	month = oct,
	title = {Hierarchical priors for Hyperspherical Prototypical Networks},
	url = {https://www.esann.org/sites/default/files/proceedings/2023/ES2023-65.pdf},
	year = {2023},
	bdsk-url-1 = {https://www.esann.org/sites/default/files/proceedings/2023/ES2023-65.pdf},
	bdsk-url-2 = {https://doi.org/10.14428/esann/2023.ES2023-65}}

@inproceedings{23:iciap:fedmed:ws:fonio,
	abstract = {This paper presents a comprehensive benchmarking study of various Federated Learning (FL) frameworks applied to the task of Medical Image Classification. The research specifically addresses the often neglected and complex aspects of scalability and usability in off-the-shelf FL frameworks. Through experimental validation using real case deployments, we provide empirical evidence of the performance and practical relevance of open source FL frameworks. Our findings contribute valuable insights for anyone interested in deploying a FL system, with a particular focus on the healthcare domain---an increasingly attractive field for FL applications.},
	address = {Udine, Italy},
	author = {Fonio, Samuele},
	booktitle = {Image Analysis and Processing - ICIAP 2023 Workshops. ICIAP 2023},
	date-modified = {2024-04-01 11:12:11 +0200},
	doi = {10.1007/978-3-031-51026-7_20},
	editor = {Foresti, G.L. and Fusiello, A. and Hancock, E.},
	keywords = {icsc, eupilot, ai},
	month = sep,
	publisher = {Springer, Cham},
	title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-51026-7_20},
	volume = {14366},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/c6be8be7-3980-4c4c-874e-68b6fd855ebc/FedMed23-3.pdf}}

@article{Antelmi_CSUR_23,
	abstract = {Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects - most commonly nodes - of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.},
	address = {New York, NY, USA},
	author = {Antelmi, Alessia and Cordasco, Gennaro and Polato, Mirko and Scarano, Vittorio and Spagnuolo, Carmine and Yang, Dingqi},
	doi = {10.1145/3605776},
	issn = {0360-0300},
	journal = {{ACM Comput. Surv.}},
	keywords = {analytics},
	month = jun,
	publisher = {Association for Computing Machinery},
	title = {A Survey on Hypergraph Representation Learning},
	url = {https://doi.org/10.1145/3605776},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1145/3605776}}

@inproceedings{Antelmi_IC2S2_2023,
	abstract = {This work investigates the developers' behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users' posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these QA platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.},
	author = {Antelmi, Alessia and De Vinco, Daniele and Cordasco, Gennaro and Spagnuolo, Carmine},
	booktitle = {International Conference on Computational Social Science 2023},
	keywords = {icsc, analytics},
	title = {Towards Unraveling Developers Communities in Stack Overflow and Reddit},
	url = {https://openreview.net/forum?id=WP5ZaAFP19},
	year = {2023},
	bdsk-url-1 = {https://openreview.net/forum?id=WP5ZaAFP19}}

@inproceedings{Antelmi_TEL4FC_2023,
	abstract = {The increasing availability of Open Data gives birth to a fertile field for interested stakeholders to create value out of them; however, limited technical expertise and poor awareness are crucial barriers to their exploitation. Because of these reasons, there is an urge for learners to acquire data and information literacy competencies, which are essential for 21st-century skills, and become familiar with available Open Data sources and their potential uses. To promote the dialogue around activities to boost recognition of Open Data and improve users' skills to work with them, we proposed a series of workshops to introduce Italian high school learners to searching for, authoring, and building effective communication based on Open Data. This article describes an ongoing activity and details its organization, reports preliminary results on learners' engagement, and discusses both challenges of the remote setting as well as promising learning outcomes.},
	address = {Cham},
	author = {Antelmi, Alessia},
	booktitle = {Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference},
	keywords = {icsc, analytics},
	publisher = {Springer International Publishing},
	title = {Engagement in Open Data Workshops: The dark side of remote settings},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-42134-1_33},
	year = {2023},
	bdsk-url-1 = {https://link.springer.com/chapter/10.1007/978-3-031-42134-1_33}}

@inproceedings{23:medic:formal-model,
	abstract = {Designing complex applications and executing them on large-scale topologies of heterogeneous architectures is becoming increasingly crucial in many scientific domains. As a result, diverse workflow modelling paradigms are developed, most of them with no formalisation provided. In these circumstances, comparing two different models or switching from one system to the other becomes a hard nut to crack. This paper investigates the capability of process algebra to model a location aware workflow system. Distributed $\pi$-calculus is considered as the base of the formal model due to its ability to describe the communicating components that change their structure as an outcome of the communication. Later, it is discussed how the base model could be extended or modified to capture different features of location aware workflow system. The intention of this paper is to highlight the fact that due to its flexibility, $\pi$-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.},
	address = {Torino, Italy},
	author = {Doriana Medi\'{c} and Marco Aldinucci},
	booktitle = {47th {IEEE} Annual Computers, Software, and Applications Conference, {COMPSAC} 2023},
	doi = {10.1109/COMPSAC57700.2023.00289},
	editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji{-}Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},
	keywords = {eupex, icsc, semantics},
	pages = {1864--1869},
	publisher = {{IEEE}},
	title = {Towards formal model for location aware workflows},
	url = {https://iris.unito.it/retrieve/1f9f959c-cd88-4d9c-90ea-54f1c86a15bc/6210-medic.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/1f9f959c-cd88-4d9c-90ea-54f1c86a15bc/6210-medic.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/COMPSAC57700.2023.00289}}

@inproceedings{23:mulone:wide:vcp,
	abstract = {In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.},
	address = {Torino, Italy},
	author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},
	booktitle = {47th {IEEE} Annual Computers, Software, and Applications Conference, {COMPSAC} 2023},
	doi = {10.1109/COMPSAC57700.2023.00288},
	editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji{-}Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},
	keywords = {across, icsc, streamflow},
	pages = {1858--1863},
	publisher = {{IEEE}},
	title = {Porting the {V}ariant {C}alling {P}ipeline for {NGS} data in cloud-{HPC} environment},
	url = {https://iris.unito.it/bitstream/2318/1919364/1/paper.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/bitstream/2318/1919364/1/paper.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/COMPSAC57700.2023.00288}}

@article{23:casella:ultra,
	abstract = {Background: Data about the long-term performance of new-generation ultrathin-strut drug-eluting stents (DES) in challenging coronary lesions, such as left main (LM), bifurcation, and chronic total occlusion (CTO) lesions are scant. Methods: The international multicenter retrospective observational ULTRA study included consecutive patients treated from September 2016 to August 2021 with ultrathin-strut DES in challenging de novo lesions. Primary endpoint was target lesion failure (TLF): composite of cardiac death, target-lesion revascularization (TLR), target-vessel myocardial infarction (TVMI), or definite stent thrombosis (ST). Secondary endpoints included all-cause death, acute myocardial infarction (AMI), target vessel revascularization, and TLF components. TLF predictors were assessed with Cox multivariable analysis. Results: Of 1801 patients (age: 66.6$\pm$11.2 years; male: 1410 [78.3\%]), 170 (9.4\%) experienced TLF during follow-up of 3.1$\pm$1.4 years. In patients with LM, CTO, and bifurcation lesions, TLF rates were 13.5\%, 9.9\%, and 8.9\%, respectively. Overall, 160 (8.9\%) patients died (74 [4.1\%] from cardiac causes). AMI and TVMI rates were 6.0\% and 3.2\%, respectively. ST occurred in 11 (1.1\%) patients while 77 (4.3\%) underwent TLR. Multivariable analysis identified the following predictors of TLF: age, STEMI with cardiogenic shock, impaired left ventricular ejection fraction, diabetes, and renal dysfunction. Among the procedural variables, total stent length increased TLF risk (HR: 1.01, 95\% CI: 1-1.02 per mm increase), while intracoronary imaging reduced the risk substantially (HR: 0.35, 95\% CI: 0.12-0.82). Conclusions: Ultrathin-strut DES showed high efficacy and satisfactory safety, even in patients with challenging coronary lesions. Yet, despite using contemporary gold-standard DES, the association persisted between established patient- and procedure-related features of risk and impaired 3-year clinical outcome.},
	author = {De Filippo, Ovidio and Bruno, Francesco and Pinxterhuis, Tineke H. and Gasior, Mariusz and Perl, Leor and Gaido, Luca and Tuttolomondo, Domenico and Greco, Antonio and Verardi, Roberto and Lo Martire, Gianluca and Iannaccone, Mario and Leone, Attilio and Liccardo, Gaetano and Caglioni, Serena and Gonz{\'a}lez Ferreiro, Rocio and Rodin{\`o}, Giulio and Musumeci, Giuseppe and Patti, Giuseppe and Borzillo, Irene and Tarantini, Giuseppe and Wa{\'n}ha, Wojciech and Casella, Bruno and Ploumen, Eline H and Pyka, Lukasz and Kornowski, Ran and Gagnor, Andrea and Piccolo, Raffaele and Roubin, Sergio Raposeiras and Capodanno, Davide and Zocca, Paolo and Conrotto, Federico and de Ferrari, Gaetano Maria and von Birgelen, Clemens and D'Ascenzo, Fabrizio},
	date-modified = {2025-05-24 10:56:27 +0200},
	doi = {10.1002/ccd.30696},
	journal = {Catheterization and Cardiovascular Interventions},
	keywords = {ai, cardio},
	title = {Predictors of target lesion failure after treatment of left main, bifurcation, or chronic total occlusion lesions with ultrathin-strut drug-eluting coronary stents in the ULTRA registry},
	url = {https://onlinelibrary.wiley.com/doi/full/10.1002/ccd.30696},
	year = {2023},
	bdsk-url-1 = {https://onlinelibrary.wiley.com/doi/full/10.1002/ccd.30696},
	bdsk-url-2 = {https://doi.org/10.1002/ccd.30696}}

@inproceedings{23:aldinucci:continuum,
	abstract = {This paper proposes a continuum-aware programming model enabling the execution of application workflows across the compute continuum: cloud, fog and edge resources. It simplifies the management of heterogeneous nodes while alleviating the burden of programmers and unleashing innovation. This model optimizes the continuum through advanced development experiences by transforming workflows into autonomous service collaborations. It reduces complexity in positioning/interconnecting services across the continuum. A meta-model introduces high-level workflow descriptions as service networks with defined contracts and quality of service, thus enabling the deployment/management of workflows as first-class entities. It also provides automation based on policies, monitoring and heuristics. Tailored mechanisms orchestrate/manage services across the continuum, optimizing performance, cost, data protection and sustainability while managing risks. This model facilitates incremental development with visibility of design impacts and seamless evolution of applications and infrastructures. In this work, we explore this new computing paradigm showing how it can trigger the development of a new generation of tools to support the compute continuum progress.},
	address = {Turin, Italy},
	author = {Aldinucci, Marco and Birke, Robert and Brogi, Antonio and Carlini, Emanuele and Coppola, Massimo and Danelutto, Marco and Dazzi, Patrizio and Ferrucci, Luca and Forti Stefano and Kavalionak, Hanna and Mencagli, Gabriele and Mordacchin, Matteo and Pasin, Marcelo and Paganelli, Federica and Torquati, Massimo},
	booktitle = {{2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)}},
	doi = {10.1109/COMPSAC57700.2023.00287},
	keywords = {icsc},
	month = jun,
	publisher = {{IEEE}},
	title = {{A Proposal for a Continuum-aware Programming Model: From Workflows to Services Autonomously Interacting in the Compute Continuum}},
	url = {https://iris.unito.it/retrieve/2ae13a33-5814-43da-8ea6-2d3e8b122384/Continuum-aware-PM.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/2ae13a33-5814-43da-8ea6-2d3e8b122384/Continuum-aware-PM.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/COMPSAC57700.2023.00287}}

@inproceedings{23:hong:datafree,
	abstract = {Deep machine learning models, e.g., image classifier, are increasingly deployed in the wild to provide services to users. Adversaries are shown capable of stealing the knowledge of these models by sending inference queries and then training substitute models based on query results. The availability and quality of adversarial query inputs are undoubtedly crucial in the stealing process. The recent prior art demonstrates the feasibility of replacing real data by exploring the synthetic adversarial queries, so called data-free attacks, under strong adversarial assumptions, i.e., the deployed classier returns not only class labels but also class probabilities. In this paper, we consider a general adversarial model and propose an effective data-free stealing algorithm, Tandem-GAN, which not only explores synthetic queries but also explicitly exploits the high quality ones. The core of TandemGAN is composed of (i) substitute model which imitates the target model through synthetic queries and their inferred labels; and (ii) a tandem generator consisting of two networks, Gx and Ge, which first explores the synthetic data space via Gx and then exploits high-quality examples via Ge to maximize the knowledge transfer from the target to the substitute model. Our results on four datasets show that the accuracy of our trained substitute model ranges between 96-67\% of the target model and outperforms the existing state-of-the-art data-free model stealing approach by up to 2.5X.},
	address = {Turin, Italy},
	author = {Hong, Chi and Huang, Jiyue and Birke, Robert and Chen, Lydia Y.},
	booktitle = {{European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)}},
	doi = {10.1007/978-3-031-43424-2_2},
	keywords = {icsc, eupilot},
	month = sep,
	title = {{Exploring and Exploiting Data-Free Model Stealing}},
	url = {https://iris.unito.it/retrieve/ce44dec6-12c9-443d-99e7-f1141e50aa3a/Data-free%20Model%20Stealing.pdf},
	year = {2023},
	bdsk-url-1 = {https://dx.doi.org/10.1007/978-3-031-43424-2_2}}

@inproceedings{23:valtari:pilot,
	abstract = {Virtualized protection and control (VPC) is seen as a promising evolution for the centralized protection and control (CPC) concept. Centralization of protection functions consolidates the functions of multiple traditional relays into one device. This consolidation reduces communications network complexity and offers effective ways to manage protection applications of the substation. Making the CPC available as a VPC software image instead of a dedicated device creates yet another degree of freedom. The solution becomes hardware independent, bringing more flexibility and scalability to the solution. ABB and Caruna together wanted to explore these possibilities in a real-life substation pilot. This paper describes the piloted VPC environment and the results from the piloting period. The results show that virtualization technology is suitable for time critical protection and control applications, with real-time performance comparable to existing non- virtualized solutions.},
	address = {Rome, Italy},
	author = {Valtari, Jani and Kulmala, Anna and Sch\"{o}nborn, Sandro and Khozaya, David and Birke, Robert and Reikko Jyrki},
	booktitle = {{27th International Conference on Electricity Distribution (CIRED)}},
	date-modified = {2024-08-18 14:40:14 +0200},
	doi = {10.1049/icp.2023.1219},
	keywords = {RT},
	month = jun,
	title = {{Real-life Pilot of Virtual Protection and Control - Experiences and Performance Analysis}},
	url = {https://iris.unito.it/retrieve/5de5fb00-02bf-4ba8-a4db-5876415d5105/virtualization_full_paper_cired2023_submitted.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/5de5fb00-02bf-4ba8-a4db-5876415d5105/virtualization_full_paper_cired2023_submitted.pdf},
	bdsk-url-2 = {https://doi.org/10.1049/icp.2023.1219}}

@inproceedings{23:schoenborn:vipac,
	abstract = {Substation automation is ever challenged by the integration of distributed energy resources which imposes higher deployment flexibility and adaptability for protection and control. Although virtualization helps to run software applications independent of the underlying platform in IT infrastructures and cloud computing, it is still not commonly used in the field of substation automation. This is mainly due to the real-time performance demands of substation automation protection and control applications. In this article, we present an approach for running substation automation protection and control software in virtual environments. We contrast the real-time performance of different virtualization technologies under different workloads and focus on the performance evaluation of protection and control software in container- based solutions running on Linux with PREEMPT RT. We also present additional results for performance achieved in virtual machines. Our results clearly demonstrate that it is possible to run substation automation protection and control software in virtual environments while still providing the necessary performance. This paves the way for the deployment of substation protection and control software in virtualisation environments.},
	address = {Rome, Italy},
	author = {Sch\"{o}nborn, Sandro and Birke, Robert and Kozhaya, David and Sivanthi, Thanikesavan},
	booktitle = {{27th International Conference on Electricity Distribution (CIRED)}},
	date-modified = {2024-08-18 14:39:46 +0200},
	doi = {10.1049/icp.2023.1028},
	keywords = {RT},
	month = jun,
	title = {{Real-Time Performance of Virtualised Protection and Control Software}},
	url = {https://iris.unito.it/retrieve/eb610327-6e38-4f5e-8673-e62f2d956821/10702-Scho%cc%88nborn.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/eb610327-6e38-4f5e-8673-e62f2d956821/10702-Scho%cc%88nborn.pdf},
	bdsk-url-2 = {https://doi.org/10.1049/icp.2023.1028}}

@inproceedings{23:mittone:mafl,
	abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. We test MAFL from multiple points of view, assessing its correctness, flexibility and scaling properties up to 64 nodes. We optimised the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},
	address = {Limassol, Cyprus},
	author = {Mittone, Gianluca and Riviera, Walter and Colonnelli, Iacopo and Birke, Robert and Aldinucci, Marco},
	booktitle = {Euro-Par 2023: Parallel Processing},
	date-added = {2023-03-8 21:51:14 +0000},
	date-modified = {2024-08-18 13:15:50 +0200},
	doi = {10.1007/978-3-031-39698-4_26},
	institution = {Computer Science Department, University of Torino},
	keywords = {eupilot, icsc, riscv, fl},
	month = aug,
	pages = {383--396},
	publisher = {{Springer}},
	title = {Model-Agnostic Federated Learning},
	url = {https://hdl.handle.net/2318/1928750},
	volume = {14100},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.48550/arXiv.2303.04906}}

@inproceedings{23:mittone:fl-riscv,
	abstract = {Decentralised Machine Learning (DML) enables collaborative machine learning without centralised input data. Federated Learning (FL) and Edge Inference are examples of DML. While tools for DML (especially FL) are starting to flourish, many are not flexible and portable enough to experiment with novel systems (e.g., RISC-V), non-fully connected topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library. We experiment with it by generating different working DML schemes on two emerging architectures (ARM-v8, RISC-V) and the x86-64 platform. We characterise the performance and energy efficiency of the presented schemes and systems. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},
	address = {Bologna, Italy},
	author = {Mittone, Gianluca and Tonci, Nicol{\`o} and Birke, Robert and Colonnelli, Iacopo and Medi\'{c}, Doriana and Bartolini, Andrea and Esposito, Roberto and Parisi, Emanuele and Beneventi, Francesco and Polato, Mirko and Torquati, Massimo and Benini, Luca and Aldinucci, Marco},
	booktitle = {20th {ACM} International Conference on Computing Frontiers ({CF} '23)},
	date-added = {2023-03-14 15:34:00 +0000},
	date-modified = {2024-08-18 13:15:37 +0200},
	doi = {10.1145/3587135.3592211},
	institution = {Computer Science Department, University of Torino},
	isbn = {979-8-4007-0140-5/23/05},
	keywords = {eupilot, icsc, hpc, riscv, fl},
	month = may,
	note = {https://arxiv.org/abs/2302.07946},
	publisher = {{ACM}},
	title = {Experimenting with Emerging {RISC-V} Systems for Decentralised Machine Learning},
	url = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592211},
	year = {2023},
	bdsk-url-1 = {https://dl.acm.org/doi/pdf/10.1145/3587135.3592211},
	bdsk-url-2 = {https://doi.org/10.1145/3587135.3592211}}

@inproceedings{23:mittone:dti,
	abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain has the potential to deliver life-saving breakthroughs. It is, however, notoriously difficult due to regulatory constraints and commercial interests. This work proposes the application of federated learning, which we argue to be reconcilable with the industry's constraints, as it does not require sharing of any information that would reveal the entities' data or any other high-level summary of it. When used on a representative GraphDTA model and the KIBA dataset it achieves up to 15 percent improved performance relative to the best available non-privacy preserving alternative. Our extensive battery of experiments shows that, unlike in other domains, the non-IID data distribution in the DTI datasets does not deteriorate FL performance. Additionally, we identify a material trade-off between the benefits of adding new data, and the cost of adding more clients.},
	address = {Austin, Texas},
	author = {Mittone, Gianluca and Svoboda, Filip and Aldinucci, Marco and Lane, Nicholas D. and Li{\`o}, Pietro},
	booktitle = {Companion Proceedings of the ACM Web Conference 2023 (WWW '23 Companion)},
	date-added = {2023-03-14 15:34:00 +0000},
	date-modified = {2024-08-18 13:15:26 +0200},
	doi = {10.1145/3543873.3587687},
	institution = {Computer Science Department, University of Torino},
	isbn = {978-1-4503-9419-2/23/04},
	keywords = {eupilot, icsc, fl},
	month = apr,
	note = {https://arxiv.org/abs/2302.07684},
	publisher = {{ACM}},
	title = {A Federated Learning Benchmark for Drug-Target Interaction},
	url = {https://hdl.handle.net/2318/1898472},
	year = {2023},
	bdsk-url-1 = {https://hdl.handle.net/2318/1898472},
	bdsk-url-2 = {https://doi.org/10.1145/3543873.3587687}}

@inproceedings{22:ml4astro,
	abstract = {HPC and AI are fated to meet for several reasons. This article will discuss some of them and argue why this will happen through the set of methods and technologies that underpin cloud computing. As a paradigmatic example, we present a new federated learning system that collaboratively trains a deep learning model in different supercomputing centers. The system is based on the StreamFlow workflow manager designed for hybrid cloud-HPC infrastructures.},
	address = {Catania, Italy},
	author = {Colonnelli, Iacopo and Casella, Bruno and Mittone, Gianluca and Arfat, Yasir and Cantalupo, Barbara and Esposito, Roberto and Martinelli, Alberto R. and Medi\'{c}, Doriana and Aldinucci, Marco},
	booktitle = {Astrophysics and Space Science Proceedings},
	date-modified = {2024-04-01 11:12:57 +0200},
	doi = {10.1007/978-3-031-34167-0_39},
	editor = {Bufano, Filomena and Riggi, Simone and Sciacca, Eva and Schillir\`{o}, Francesco},
	isbn = {978-3-031-34167-0},
	keywords = {across, eupilot, streamflow, fl},
	pages = {193--199},
	publisher = {Springer},
	title = {Federated Learning meets {HPC} and cloud},
	url = {https://iris.unito.it/retrieve/3ac66baa-9d9a-4e9f-94a5-13700694d8aa/ML4Astro.pdf},
	volume = {60},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/3ac66baa-9d9a-4e9f-94a5-13700694d8aa/ML4Astro.pdf}}

@inproceedings{23:praise-fl:pdp,
	abstract = {Federated Learning (FL) is becoming popular in different industrial sectors where data access is critical for security, privacy and the economic value of data itself. Unlike traditional machine learning, where all the data must be globally gathered for analysis, FL makes it possible to extract knowledge from data distributed across different organizations that can be coupled with different Machine Learning paradigms. In this work, we replicate, using Federated Learning, the analysis of a pooled dataset (with AdaBoost) that has been used to define the PRAISE score, which is today among the most accurate scores to evaluate the risk of a second acute myocardial infarction. We show that thanks to the extended-OpenFL framework, which implements AdaBoost.F, we can train a federated PRAISE model that exhibits comparable accuracy and recall as the centralised model. We achieved F1 and F2 scores which are consistently comparable to the PRAISE score study of a 16- parties federation but within an order of magnitude less time.},
	address = {Napoli, Italy},
	author = {Yasir Arfat and Gianluca Mittone and Iacopo Colonnelli and Fabrizio D'Ascenzo and Roberto Esposito and Marco Aldinucci},
	booktitle = {31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing, {PDP} 2023},
	date-added = {2023-02-04 18:16:36 +0100},
	date-modified = {2024-08-18 13:16:20 +0200},
	doi = {10.1109/PDP59025.2023.00057},
	keywords = {admire, cardio, fl},
	pages = {329--337},
	publisher = {IEEE},
	title = {Pooling critical datasets with Federated Learning},
	url = {https://iris.unito.it/retrieve/491e22ec-3db5-4989-a063-085a199edd20/23_pdp_fl.pdf},
	year = {2023},
	bdsk-url-1 = {https://iris.unito.it/retrieve/491e22ec-3db5-4989-a063-085a199edd20/23_pdp_fl.pdf}}

@inbook{Contaldo2023,
	abstract = {The idea behind novel single-cell RNA sequencing (scRNA-seq) pipelines is to isolate single cells through microfluidic approaches and generate sequencing libraries in which the transcripts are tagged to track their cell of origin. Modern scRNA-seq platforms are capable of analyzing up to many thousands of cells in each run. Then, combined with massive high-throughput sequencing producing billions of reads, scRNA-seq allows the assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution.},
	address = {New York, NY},
	author = {Contaldo, Sandro Gepiro and Alessandri, Luca and Colonnelli, Iacopo and Beccuti, Marco and Aldinucci, Marco},
	booktitle = {Single Cell Transcriptomics: Methods and Protocols},
	doi = {10.1007/978-1-0716-2756-3_17},
	editor = {Calogero, Raffaele Adolfo and Benes, Vladimir},
	isbn = {978-1-0716-2756-3},
	keywords = {streamflow},
	pages = {337--345},
	publisher = {Springer {US}},
	title = {Bringing Cell Subpopulation Discovery on a Cloud-{HPC} Using {rCASC} and {StreamFlow}},
	url = {https://datacloud.di.unito.it/index.php/s/KMfKo4m7GTGdZmF},
	year = {2023},
	bdsk-url-1 = {https://doi.org/10.1007/978-1-0716-2756-3_17}}

@inproceedings{sdm-ghiassi23,
	abstract = {Learning robust deep models against noisy labels becomes ever critical when today's data is commonly collected from open platforms and subject to adversarial corruption. The information on the label corruption process, i.e., corruption matrix, can greatly enhance the robustness of deep models but still fall behind in combating hard classes. In this paper, we propose to construct a golden symmetric loss (GSL) based on the estimated corruption matrix as to avoid overfitting to noisy labels and learn effectively from hard classes. GSL is the weighted sum of the corrected regular cross entropy and reverse cross entropy. By leveraging a small fraction of trusted clean data, we estimate the corruption matrix and use it to correct the loss as well as to determine the weights of GSL. We theoretically prove the robustness of the proposed loss function in the presence of dirty labels. We provide a heuristics to adaptively tune the loss weights of GSL according to the noise rate and diversity measured from the dataset. We evaluate our proposed golden symmetric loss on both vision and natural language deep models subject to different types of label noise patterns. Empirical results show that GSL can significantly outperform the existing robust training methods on different noise patterns, showing accuracy improvement up to 18% on CIFAR-100 and 1% on real world noisy dataset of Clothing1M.},
	author = {Ghiassi, Amirmasoud and Birke, Robert and Chen, Lydia Y.},
	booktitle = {{SDM} '23: {SIAM} International Conference on Data Mining},
	doi = {10.1137/1.9781611977653.ch64},
	keywords = {textarossa},
	pages = {568--576},
	title = {Robust Learning via Golden Symmetric Loss of (un)Trusted Labels},
	url = {https://datacloud.di.unito.it/index.php/s/b6z3moNLxnNiCxz},
	year = {2023},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/b6z3moNLxnNiCxz},
	bdsk-url-2 = {https://doi.org/10.1137/1.9781611977653.ch64}}

@inproceedings{DBLP:conf/csedu/PellegrinoA23,
  author       = {Maria Angela Pellegrino and
                  Alessia Antelmi},
  editor       = {Jelena Jovanovic and
                  Irene{-}Angelica Chounta and
                  James Uhomoibhi and
                  Bruce M. McLaren},
  title        = {At School of Open Data: {A} Literature Review},
  booktitle    = {Proceedings of the 15th International Conference on Computer Supported
                  Education, {CSEDU} 2023, Prague, Czech Republic, April 21-23, 2023,
                  Volume 2},
  pages        = {172--183},
  publisher    = {{SCITEPRESS}},
  year         = {2023},
  url          = {https://doi.org/10.5220/0011747500003470},
  doi          = {10.5220/0011747500003470},
  timestamp    = {Wed, 29 May 2024 11:49:33 +0200},
  biburl       = {https://dblp.org/rec/conf/csedu/PellegrinoA23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/www/AntelmiCVS23,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Daniele De Vinco and
                  Carmine Spagnuolo},
  editor       = {Ying Ding and
                  Jie Tang and
                  Juan F. Sequeda and
                  Lora Aroyo and
                  Carlos Castillo and
                  Geert{-}Jan Houben},
  title        = {The Age of Snippet Programming: Toward Understanding Developer Communities
                  in Stack Overflow and Reddit},
  booktitle    = {Companion Proceedings of the {ACM} Web Conference 2023, {WWW} 2023,
                  Austin, TX, USA, 30 April 2023 - 4 May 2023},
  pages        = {1218--1224},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3543873.3587673},
  doi          = {10.1145/3543873.3587673},
  timestamp    = {Sun, 19 Jan 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/www/AntelmiCVS23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{GARCIA:Computing:22,
	abstract = {In a fast-changing data-driven world, real-time data processing systems are becoming ubiquitous in everyday applications. The increasing data we produce, such as audio, video, image, and, text are demanding quickly and efficiently computation. Stream Parallelism allows accelerating this computation for real-time processing. But it is still a challenging task and most reserved for experts. In this paper, we present SPBench, a framework for benchmarking stream processing applications. It aims to support users with a set of real-world stream processing applications, which are made accessible through an Application Programming Interface (API) and executable via Command Line Interface (CLI) to create custom benchmarks. We tested SPBench by implementing parallel benchmarks with Intel Threading Building Blocks (TBB), FastFlow, and SPar. This evaluation provided useful insights and revealed the feasibility of the proposed framework in terms of usage, customization, and performance analysis. SPBench demonstrated to be a high-level, reusable, extensible, and easy of use abstraction to build parallel stream processing benchmarks on multi-core architectures.},
	author = {Garcia, Adriano Marques and Griebler, Dalvan and Schepke, Claudio and Fernandes, Luiz Gustavo},
	date-modified = {2024-08-18 14:42:47 +0200},
	doi = {10.1007/s00607-021-01025-6},
	isbn = {1436-5057},
	journal = {Computing},
	keywords = {parallel},
	number = {5},
	pages = {1077--1099},
	publisher = {Springer},
	title = {SPBench: a framework for creating benchmarks of stream processing applications},
	url = {https://iris.unito.it/retrieve/f17ea8c2-ddd8-425b-b4e7-8315218a6969/s00607-021-01025-6_preprint.pdf},
	volume = {105},
	year = {2022},
	bdsk-url-1 = {https://iris.unito.it/retrieve/f17ea8c2-ddd8-425b-b4e7-8315218a6969/s00607-021-01025-6_preprint.pdf}}

@inproceedings{GARCIA:PDP:22,
	abstract = {In stream processing, data arrives constantly and is often unpredictable. It can show large fluctuations in arrival frequency, size, complexity, and other factors. These fluctuations can strongly impact application latency and throughput, which are critical factors in this domain. Therefore, there is a significant amount of research on self-adaptive techniques involving elasticity or micro-batching as a way to mitigate this impact. However, there is a lack of benchmarks and tools for helping researchers to investigate micro-batching and data stream frequency implications. In this paper, we extend a benchmarking framework to support dynamic micro-batching and data stream frequency management. We used it to create custom benchmarks and compare latency and throughput aspects from two different parallel libraries. We validate our solution through an extensive analysis of the impact of micro-batching and data stream frequency on stream processing applications using Intel TBB and FastFlow, which are two libraries that leverage stream parallelism on multi-core architectures. Our results demonstrated up to 33\% throughput gain over latency using micro-batches. Additionally, while TBB ensures lower latency, FastFlow ensures higher throughput in the parallel applications for different data stream frequency configurations.},
	address = {Valladolid, Spain},
	author = {Garcia, Adriano Marques and Griebler, Dalvan and Schepke, Claudio and Fernandes, Luiz Gustavo},
	booktitle = {30th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
	date-modified = {2024-08-18 14:42:03 +0200},
	doi = {10.1109/PDP55904.2022.00011},
	keywords = {parallel},
	month = apr,
	pages = {10--17},
	publisher = {IEEE},
	series = {PDP'22},
	title = {Evaluating Micro-batch and Data Frequency for Stream Processing Applications on Multi-cores},
	url = {https://iris.unito.it/retrieve/f6d113e5-789b-4f8b-924d-8ca3d38e8d62/PDP_2022__SPBench_with_Batch_and_Data_Frequency_.pdf},
	year = {2022},
	bdsk-url-1 = {https://iris.unito.it/retrieve/f6d113e5-789b-4f8b-924d-8ca3d38e8d62/PDP_2022__SPBench_with_Batch_and_Data_Frequency_.pdf}}

@inproceedings{DBLP:conf/embc/SalanitriBPIWBE22,
	author = {Proietto Salanitri, Federica and Bellitto, Giovanni and Palazzo, Simone and Irmakci, Ismail and Wallace, Michael B. and Bolan, Candice W. and Engels, Megan and Hoogenboom, Sanne and Aldinucci, Marco and Bagci, Ulas and Giordano, Daniela and Spampinato, Concetto},
	booktitle = {44th Annual International Conference of the {IEEE} Engineering in Medicine {\&} Biology Society, {EMBC} 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022},
	doi = {10.1109/EMBC48229.2022.9871547},
	keywords = {hpc4ai},
	pages = {475--479},
	publisher = {{IEEE}},
	title = {Neural Transformers for Intraductal Papillary Mucosal Neoplasms {(IPMN)} Classification in {MRI} images},
	url = {https://doi.org/10.1109/EMBC48229.2022.9871547},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1109/EMBC48229.2022.9871547}}

@article{CESARE2022100660,
	abstract = {The Gaia Astrometric Verification Unit---Global Sphere Reconstruction (AVU--GSR) Parallel Solver aims to find the astrometric parameters for circa $10^8$ stars in the Milky Way, the attitude and the instrumental specifications of the Gaia satellite, and the global parameter y of the post Newtonian formalism.  The code iteratively solves a system of linear equations,A x = b, where the coefficient matrix A is large (circa $10^11 x 10^8$ elements) and sparse. To solve this system of equations, the code exploits a hybrid implementation of the iterative PC-LSQR algorithm, where the computation related to different horizontal portions of the coefficient matrix is assigned to separate MPI processes. In the original code, each matrix portion is further parallelized over the OpenMP threads. To further improve the code performance, we ported the application to the GPU, replacing the OpenMP parallelization language with OpenACC. In this port,  95\% of the data is copied from the host to the device at the beginning of the entire cycle of iterations, making the code compute bound rather than data-transfer bound. The OpenACC code presents a speedup of circa 1.5 over the OpenMP version but further optimizations are in progress to obtain higher gains. The code runs on multiple GPUs, and it was tested on the CINECA supercomputer Marconi100, in anticipation of a port to the pre-exascale system Leonardo, which will be installed at CINECA in 2022.},
	author = {Cesare, Valentina and Becciani, Ugo and Vecchiato, Alberto and Lattanzi, Mario Gilberto and Pitari, Fabio and Raciti, Mario and Tudisco, Giuseppe and Aldinucci, Marco and Bucciarelli, Beatrice},
	date-modified = {2025-05-24 10:54:44 +0200},
	doi = {10.1016/j.ascom.2022.100660},
	issn = {2213-1337},
	journal = {Astronomy and Computing},
	keywords = {astro, eupex},
	pages = {100660},
	title = {The Gaia {AVU-GSR} parallel solver: Preliminary studies of a {LSQR-based} application in perspective of exascale systems},
	url = {https://openaccess.inaf.it/handle/20.500.12386/32451},
	year = {2022},
	bdsk-url-1 = {https://openaccess.inaf.it/handle/20.500.12386/32451},
	bdsk-url-2 = {https://doi.org/10.1016/j.ascom.2022.100660}}

@inproceedings{22:eage-hpc-workshop,
	abstract = {Large-scale scientific applications are facing an irrevrsible transition from monolithic, high-performance oriented codes to modular and polyglot deployments of specialised (micro-)services. The reasons behind this transition are many: coupling of standard solvers with Deep Learning techniques, offloading of data analysis and visualisation to Cloud, and the advent of specialised hardware accelerators. Topology-aware Workflow Management Systems (WMSs) play a crucial role. In particular, topology-awareness allows an explicit mapping of workflow steps onto heterogeneous locations, allowing automated executions on top of hybrid architectures (e.g., cloud+HPC or classical+quantum). Plus, topology-aware WMSs can offer nonfunctional requirements OOTB, e.g. components' life-cycle orchestration, secure and efficient data transfers, fault tolerance, and cross-cluster execution of urgent workloads. Augmenting interactive Jupyter Notebooks with distributed workflow capabilities allows domain experts to prototype and scale applications using the same technological stack, while relying on a feature-rich and user-friendly web interface. This abstract will showcase how these general methodologies can be applied to a typical geoscience simulation pipeline based on the Full Wavefront Inversion (FWI) technique. In particular, a prototypical Jupyter Notebook will be executed interactively on Cloud. Preliminary data analyses and post-processing will be executed locally, while the computationally demanding optimisation loop will be scheduled on a remote HPC cluster.},
	address = {Milano, Italy},
	author = {Iacopo Colonnelli and Marco Aldinucci},
	booktitle = {Sixth {EAGE} High Performance Computing Workshop},
	doi = {10.3997/2214-4609.2022615029},
	issn = {2214-4609},
	keywords = {across, eupex},
	month = sep,
	pages = {1--5},
	publisher = {{European Association of Geoscientists \& Engineers }},
	title = {Hybrid Workflows For Large - Scale Scientific Applications},
	url = {https://iris.unito.it/retrieve/d79ddabb-f9d7-4a55-9f84-1528b1533ba3/Extended_Abstract.pdf},
	year = {2022},
	bdsk-url-1 = {https://iris.unito.it/retrieve/d79ddabb-f9d7-4a55-9f84-1528b1533ba3/Extended_Abstract.pdf},
	bdsk-url-2 = {https://doi.org/10.3997/2214-4609.2022615029}}

@article{textarossa2022micpro,
	abstract = {In the near future, Exascale systems will need to bridge three technology gaps to achieve high performance while remaining under tight power constraints: energy efficiency and thermal control; extreme computation efficiency via HW acceleration and new arithmetic; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA addresses these gaps through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models, and tools derived from European research.},
	author = {Agosta, Giovanni and Aldinucci, Marco and Alvarez, Carlos and Ammendola, Roberto and Arfat, Yasir and Beaumont, Olivier and Bernaschi, Massimo and Biagioni, Andrea and Boccali, Tommaso and Bramas, Berenger and Brandolese, Carlo and Cantalupo, Barbara and Carrozzo, Mauro and Cattaneo, Daniele and Celestini, Alessandro and Celino, Massimo and Colonnelli, Iacopo and Cretaro, Paolo and D'Ambra, Pasqua and Danelutto, Marco and Esposito, Roberto and Eyraud-Dubois, Lionel and Filgueras, Antonio and Fornaciari, William and Frezza, Ottorino and Galimberti, Andrea and Giacomini, Francesco and Goglin, Brice and Gregori, Daniele and Guermouche, Abdou and Iannone, Francesco and Kulczewski, Michal and Lo Cicero, Francesca and Lonardo, Alessandro and Martinelli, Alberto R. and Martinelli, Michele and Martorell, Xavier and Massari, Giuseppe and Montangero, Simone and Mittone, Gianluca and Namyst, Raymond and Oleksiak, Ariel and Palazzari, Paolo and Stanislao Paolucci, Pier and Reghenzani, Federico and Rossi, Cristian and Saponara, Sergio and Simula, Francesco and Terraneo, Federico and Thibault, Samuel and Torquati, Massimo and Turisini, Matteo and Vicini, Piero and Vidal, Miquel and Zoni, Davide and Zummo, Giuseppe},
	date-modified = {2025-05-24 09:50:45 +0200},
	doi = {10.1016/j.micpro.2022.104679},
	issn = {0141-9331},
	journal = {Microprocessors and Microsystems},
	keywords = {textarossa, system-sw, hpc},
	pages = {104679},
	title = {Towards EXtreme scale technologies and accelerators for euROhpc hw/Sw supercomputing applications for exascale: The TEXTAROSSA approach},
	volume = {95},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1016/j.micpro.2022.104679}}

@inproceedings{casella2022benchmarking,
	abstract = {Classic Machine Learning (ML) techniques require training on data available in a single data lake (either centralized or distributed). However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres. Federated Learning (FL) is a distributed machine learning approach which has emerged as an effective way to address privacy concerns by only sharing local AI models while keeping the data decentralized. Two critical challenges of Federated Learning are managing the heterogeneous systems in the same federated network and dealing with real data, which are often not independently and identically distributed (non-IID) among the clients. In this paper, we focus on the second problem, i.e., the problem of statistical heterogeneity of the data in the same federated network. In this setting, local models might be strayed far from the local optimum of the complete dataset, thus possibly hindering the convergence of the federated model. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv), aiming at tackling the non-IID setting, have already been proposed. This work provides an empirical assessment of the behaviour of FedAvg and FedCurv in common non-IID scenarios. Results show that the number of epochs per round is an important hyper-parameter that, when tuned appropriately, can lead to significant performance gains while reducing the communication cost. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.},
	author = {Bruno Casella and Roberto Esposito and Carlo Cavazzoni and Marco Aldinucci},
	booktitle = {Proceedings of the 1st Italian Conference on Big Data and Data Science, {ITADATA} 2022, September 20-21, 2022},
	date-modified = {2024-04-01 11:14:22 +0200},
	editor = {Anisetti, Marco and Bonifati, Angela and Bena, Nicola and Ardagna, Claudio A. and Malerba, Donato},
	keywords = {eupilot},
	publisher = {CEUR-WS.org},
	series = {{CEUR} Workshop Proceedings},
	title = {Benchmarking FedAvg and FedCurv for Image Classification Tasks},
	url = {https://ceur-ws.org/Vol-3340/paper40.pdf},
	volume = {3340},
	year = {2022},
	bdsk-url-1 = {https://iris.unito.it/bitstream/2318/1870961/1/Benchmarking_FedAvg_and_FedCurv_for_Image_Classification_Tasks.pdf}}

@inproceedings{22:fl:ijcnn,
	abstract = {Federated Learning has been proposed to develop better AI systems without compromising the privacy of final users and the legitimate interests of private companies. Initially deployed by Google to predict text input on mobile devices, FL has been deployed in many other industries. Since its introduction, Federated Learning mainly exploited the inner working of neural networks and other gradient descent-based algorithms by either exchanging the weights of the model or the gradients computed during learning. While this approach has been very successful, it rules out applying FL in contexts where other models are preferred, e.g., easier to interpret or known to work better. This paper proposes FL algorithms that build federated models without relying on gradient descent-based methods. Specifically, we leverage distributed versions of the AdaBoost algorithm to acquire strong federated models. In contrast with previous approaches, our proposal does not put any constraint on the client-side learning models. We perform a large set of experiments on ten UCI datasets, comparing the algorithms in six non-iidness settings.},
	address = {{P}adua, {Italy}},
	author = {Mirko Polato and Roberto Esposito and Marco Aldinucci},
	booktitle = {{I}ntl. {J}oint {C}onference on {N}eural {N}etworks (IJCNN)},
	date-modified = {2025-09-23 17:56:35 +0200},
	doi = {10.1109/IJCNN55064.2022.9892284},
	keywords = {hpc4ai, eupilot, ai},
	month = jul,
	pages = {1--10},
	publisher = {{IEEE}},
	title = {Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent},
	url = {https://iris.unito.it/retrieve/03a7b692-aecc-43db-a792-874c553d9ebe/ijcnn22-internal.pdf},
	year = {2022},
	bdsk-url-1 = {https://iris.unito.it/retrieve/03a7b692-aecc-43db-a792-874c553d9ebe/ijcnn22-internal.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/IJCNN55064.2022.9892284}}

@incollection{22:TABDV,
	abstract = {At the present time, we are immersed in the convergence between Big Data, High-Performance Computing and Artificial Intelligence. Technological progress in these three areas has accelerated in recent years, forcing different players like software companies and stakeholders to move quickly. The European Union is dedicating a lot of resources to maintain its relevant position in this scenario, funding projects to implement large-scale pilot testbeds that combine the latest advances in Artificial Intelligence, High-Performance Computing, Cloud and Big Data technologies. The DeepHealth project is an example focused on the health sector whose main outcome is the DeepHealth toolkit, a European unified framework that offers deep learning and computer vision capabilities, completely adapted to exploit underlying heterogeneous High-Performance Computing, Big Data and cloud architectures, and ready to be integrated into any software platform to facilitate the development and deployment of new applications for specific problems in any sector. This toolkit is intended to be one of the European contributions to the field of AI. This chapter introduces the toolkit with its main components and complementary tools, providing a clear view to facilitate and encourage its adoption and wide use by the European community of developers of AI-based solutions and data scientists working in the healthcare sector and others.},
	address = {Cham},
	author = {Aldinucci, Marco and Atienza, David and Bolelli, Federico and Caballero, M\'{o}nica and Colonnelli, Iacopo and Flich, Jos\'{e} and Ander G\'{o}mez, Jon and Gonz\'{a}lez, David and Grana, Costantino and Grangetto, Marco and Leo, Simone and L\'{o}pez, Pedro and Oniga, Dana and Paredes, Roberto and Pireddu, Luca and Qui\~{n}ones, Eduardo and Silva, Tatiana and Tartaglione, Enzo and Zapater, Marina},
	booktitle = {Technologies and Applications for Big Data Value},
	chapter = {9},
	doi = {10.1007/978-3-030-78307-5_9},
	editor = {Edward Curry and S\"{o}ren Auer and Arne J. Berre and Andreas Metzger and Maria S. Perez and Sonja Zillner},
	isbn = {978-3-030-78307-5},
	keywords = {deephealth, streamflow},
	pages = {183--202},
	publisher = {Springer International Publishing},
	title = {The {DeepHealth} Toolkit: A Key European Free and Open-Source Software for Deep Learning and Computer Vision Ready to Exploit Heterogeneous {HPC} and {C}loud Architectures},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-030-78307-5_9.pdf},
	year = {2022},
	bdsk-url-1 = {https://link.springer.com/content/pdf/10.1007/978-3-030-78307-5_9.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-030-78307-5_9}}

@inproceedings{22:VISAPP:transferlearning,
	abstract = {It has been demonstrated that deep neural networks outperform traditional machine learning. However, deep networks lack generalisability, that is, they will not perform as good as in a new (testing) set drawn from a different distribution due to the domain shift. In order to tackle this known issue, several transfer learning approaches have been proposed, where the knowledge of a trained model is transferred into another to improve performance with different data. However, most of these approaches require additional training steps, or they suffer from catastrophic forgetting that occurs when a trained model has overwritten previously learnt knowledge. We address both problems with a novel transfer learning approach that uses network aggregation. We train dataset-specific networks together with an aggregation network in a unified framework. The loss function includes two main components: a task-specific loss (such as cross-entropy) and an aggregation loss. The proposed aggregation loss allows our model to learn how trained deep network parameters can be aggregated with an aggregation operator. We demonstrate that the proposed approach learns model aggregation at test time without any further training step, reducing the burden of transfer learning to a simple arithmetical operation. The proposed approach achieves comparable performance w.r.t. the baseline. Besides, if the aggregation operator has an inverse, we will show that our model also inherently allows for selective forgetting, i.e., the aggregated model can forget one of the datasets it was trained on, retaining information on the others.},
	author = {Bruno Casella and Alessio Chisari and Sebastiano Battiato and Mario Giuffrida},
	booktitle = {Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, {VISIGRAPP} 2022, Volume 5: VISAPP, Online Streaming, February 6-8, 2022},
	date-modified = {2024-04-01 11:13:11 +0200},
	doi = {10.5220/0010907900003124},
	editor = {Giovanni Maria Farinella and Petia Radeva and Kadi Bouatouch},
	isbn = {978-989-758-555-5},
	keywords = {ai},
	organization = {INSTICC},
	pages = {642--649},
	publisher = {SciTePress},
	title = {Transfer Learning via Test-time Neural Networks Aggregation},
	url = {https://iris.unito.it/retrieve/handle/2318/1844159/947123/TRANSFER_LEARNING_VIA_TEST_TIME_NEURAL_NETWORKS_AGGREGATION.pdf},
	year = {2022},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1844159/947123/TRANSFER_LEARNING_VIA_TEST_TIME_NEURAL_NETWORKS_AGGREGATION.pdf},
	bdsk-url-2 = {https://doi.org/10.5220/0010907900003124}}

@incollection{22:deephealth:HPCbook,
	abstract = {This chapter presents the DeepHealth HPC toolkit for an efficient execution of deep learning (DL) medical application into HPC and cloud-computing infrastructures, featuring many-core, GPU, and FPGA acceleration devices. The toolkit offers to the European Computer Vision Library and the European Distributed Deep Learning Library (EDDL), developed in the DeepHealth project as well, the mechanisms to distribute and parallelize DL operations on HPC and cloud infrastructures in a fully transparent way. The toolkit implements workflow managers used to orchestrate HPC workloads for an efficient parallelization of EDDL training operations on HPC and cloud infrastructures, and includes the parallel programming models for an efficient execution EDDL inference and training operations on many-core, GPUs and FPGAs acceleration devices.},
	address = {Boca Raton, Florida},
	author = {Qui\~{n}ones, Eduardo and Perales, Jesus and Ejarque, Jorge and Badouh, Asaf and Marco, Santiago and Auzanneau, Fabrice and Galea, Fran\c{c}ois and Gonz\'{a}lez, David and Herv\'{a}s, Jos\'{e} Ram\'{o}n and Silva, Tatiana and Colonnelli, Iacopo and Cantalupo, Barbara and Aldinucci, Marco and Tartaglione, Enzo and Tornero, Rafael and Flich, Jos\'{e} and Martinez, Jose Maria and Rodriguez, David and Catal\'{a}n, Izan and Garcia, Jorge and Hern\'{a}ndez, Carles},
	booktitle = {{HPC}, Big Data, and {AI} Convergence Towards Exascale: Challenge and Vision},
	chapter = {10},
	doi = {10.1201/9781003176664},
	editor = {Olivier Terzo and Jan Martinovi\v{c}},
	isbn = {978-1-0320-0984-1},
	keywords = {deephealth, streamflow},
	pages = {191--216},
	publisher = {{CRC} Press},
	title = {The {DeepHealth} {HPC} Infrastructure: Leveraging Heterogenous {HPC} and Cloud Computing Infrastructures for {IA}-based Medical Solutions},
	url = {https://iris.unito.it/retrieve/handle/2318/1832050/912413/Preprint.pdf},
	year = {2022},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1832050/912413/Preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1201/9781003176664}}

@article{21:FGCS:jupyflow,
	abstract = {The designers of a new coordination interface enacting complex workflows have to tackle a dichotomy: choosing a language-independent or language-dependent approach. Language-independent approaches decouple workflow models from the host code's business logic and advocate portability. Language-dependent approaches foster flexibility and performance by adopting the same host language for business and coordination code. Jupyter Notebooks, with their capability to describe both imperative and declarative code in a unique format, allow taking the best of the two approaches, maintaining a clear separation between application and coordination layers but still providing a unified interface to both aspects. We advocate the Jupyter Notebooks' potential to express complex distributed workflows, identifying the general requirements for a Jupyter-based Workflow Management System (WMS) and introducing a proof-of-concept portable implementation working on hybrid Cloud-HPC infrastructures. As a byproduct, we extended the vanilla IPython kernel with workflow-based parallel and distributed execution capabilities. The proposed Jupyter-workflow (Jw) system is evaluated on common scenarios for High Performance Computing (HPC) and Cloud, showing its potential in lowering the barriers between prototypical Notebooks and production-ready implementations.},
	author = {Colonnelli, Iacopo and Aldinucci, Marco and Cantalupo, Barbara and Padovani, Luca and Rabellino, Sergio and Spampinato, Concetto and Morelli, Roberto and Di Carlo, Rosario and Magini, Nicol{`o} and Cavazzoni, Carlo},
	doi = {10.1016/j.future.2021.10.007},
	issn = {0167-739X},
	journal = {Future Generation Computer Systems},
	keywords = {streamflow, jupyter-workflow, deephealth, across},
	pages = {282--298},
	title = {Distributed workflows with {Jupyter}},
	url = {https://www.sciencedirect.com/science/article/pii/S0167739X21003976},
	volume = {128},
	year = {2022},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0167739X21003976},
	bdsk-url-2 = {https://doi.org/10.1016/j.future.2021.10.007}}

@article{COX2022101594,
	abstract = {Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted by executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. It is of paramount importance to guarantee low response times of such multi-DNN executions as it affects not only users quality of experience but also safety. The challenge, largely unaddressed by the state of the art, is how to overcome the memory limitation of edge devices without altering the DNN models. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution and scheduling framework, which requires no modification of DNN models. The aim of Masa is to consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. The enabling features of Masa are (i) modeling inter- and intra-network dependency, (ii) leveraging complimentary memory usage of each layer, and (iii) exploring the context dependency of DNNs. We verify the correctness and scheduling optimality via mixed integer programming. We extensively evaluate two versions of Masa, context-oblivious and context-aware, on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.},
	author = {Cox, Bart and Birke, Robert and Chen, Lydia Y.},
	date-modified = {2024-04-01 11:32:22 +0200},
	doi = {https://doi.org/10.1016/j.pmcj.2022.101594},
	issn = {1574-1192},
	journal = {Pervasive and Mobile Computing},
	keywords = {ai},
	pages = {1--16},
	title = {Memory-aware and context-aware multi-DNN inference on the edge},
	url = {https://www.sciencedirect.com/science/article/pii/S1574119222000372},
	volume = {83},
	year = {2022},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S1574119222000372},
	bdsk-url-2 = {https://doi.org/10.1016/j.pmcj.2022.101594}}

@inproceedings{icpp-stewart22,
	abstract = {Short-term cache allocation grants and then revokes access to processor cache lines dynamically. For online services, short-term allocation can speed up targeted query executions and free up cache lines reserved, but normally not needed, for performance. However, in collocated settings, short-term allocation can increase cache contention, slowing down collocated query executions. To offset slowdowns, collocated services may request short-term allocation more often, making the problem worse. Short-term allocation policies manage which queries receive cache allocations and when. In collocated settings, these policies should balance targeted query speedups against slowdowns caused by recurring cache contention. We present a model-driven approach that (1) predicts response time under a given policy, (2) explores competing policies and (3) chooses policies that yield low response time for all collocated services. Our approach profiles cache usage offline, characterizes the effects of cache allocation policies using deep learning techniques and devises novel performance models for short-term allocation with online services. We tested our approach using data processing, cloud, and high-performance computing benchmarks collocated on Intel processors equipped with Cache Allocation Technology. Our models predicted median response time with 11\% absolute percent error. Short-term allocation policies found using our approach out performed state-of-the-art shared cache allocation policies by 1.2-2.3X.},
	author = {Stewart, Christopher and Morris, Nathaniel and Chen, Lydia Y. and Birke, Robert},
	booktitle = {Proceedings of the 51st International Conference on Parallel Processing ({ICPP})},
	date-modified = {2024-08-18 14:41:40 +0200},
	doi = {10.1145/3545008.3545094},
	keywords = {parallel},
	month = aug,
	pages = {31:1--31:11},
	publisher = {{ACM}},
	title = {Performance Modeling for Short-Term Cache Allocation},
	url = {https://doi.org/10.1145/3545008.3545094},
	year = {2022},
	bdsk-url-1 = {https://doi.org/10.1145/3545008.3545094}}

@inproceedings{bigdata-zhu22,
	abstract = {Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first c onduct a n e xtensive e mpirical s tudy to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67\% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find t he s uitable c olumn o rder o f i nput d ata f or CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22\%, compared to the existing synthesizers.},
	author = {Zhu, Yujin and Zhao, Zilong and Birke, Robert and Chen, Lydia Y.},
	booktitle = {{IEEE} International Conference on Big Data (Big Data)},
	date-modified = {2024-08-18 14:41:08 +0200},
	doi = {10.1109/BigData55660.2022.10020639},
	editor = {Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and Dirk Van den Poel and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan},
	keywords = {analytics},
	month = dec,
	pages = {5855--5864},
	publisher = {{IEEE}},
	title = {Permutation-Invariant Tabular Data Synthesis},
	url = {https://datacloud.di.unito.it/index.php/s/b6z3moNLxnNiCxz},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/b6z3moNLxnNiCxz},
	bdsk-url-2 = {https://doi.org/10.1109/BigData55660.2022.10020639}}

@inproceedings{ghiassi/iccart22,
	abstract = {Today, to label the massive datasets needed to train Deep Neural Networks (DNNs), cheap and error-prone methods such as crowdsourcing are used. Label aggregation methods aim to infer the true labels from noisy labels annotated by crowdsourcing workers via labels statistics features. Aggregated labels are the main data source to train deep neural networks, and their accuracy directly affects the deep neural network performance. In this paper, we argue that training DNN and aggregating labels are not two separate tasks. Incorporation between DNN training and label aggregation connects data features, noisy labels, and aggregated labels. Since each image contains valuable knowledge about its label, the data features help aggregation methods enhance their performance. We propose LABNET an iterative two-step method. Step one: the label aggregation algorithm provides labels to train the DNN. Step two: the DNN shares a representation of the data features with the label aggregation algorithm. These steps are repeated until the converging label aggregation error rate. To evaluate LABNET we conduct an extensive empirical comparison on CIFAR-10 and CIFAR-100 under different noise and worker statistics. Our evaluation results show that LABNET achieves the highest mean accuracy with an increase of at least 8% to 0.6% and lowest error rate with a reduction of 7.5% to 0.25% against existing aggregation and training methods in most cases.},
	author = {Ghiassi, Amirmasoud and Birke, Robert and Chen, Lydia Y.},
	booktitle = {14th International Conference on Agents and Artificial Intelligence ({ICAART})},
	date-modified = {2025-05-24 09:18:24 +0200},
	doi = {10.5220/0010770400003116},
	editor = {Ana Paula Rocha and Luc Steels and H. Jaap van den Herik},
	keywords = {ai},
	month = feb,
	pages = {56--66},
	publisher = {{SCITEPRESS}},
	title = {{LABNET:} {A} Collaborative Method for {DNN} Training and Label Aggregation},
	url = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010770400003116},
	year = {2022},
	bdsk-url-1 = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010770400003116},
	bdsk-url-2 = {https://doi.org/10.5220/0010770400003116}}

@article{GALLONE202218,
	abstract = {The association of left ventricular ejection fraction (LVEF) with procedural and long-term outcomes after state-of-the-art percutaneous coronary intervention (PCI) of bifurcation lesions remains unsettled. A total of 5,333 patients who underwent contemporary coronary bifurcation PCI were included in the intercontinental retrospective combined insights from the unified RAIN (veRy thin stents for patients with left mAIn or bifurcatioN in real life) and COBIS (COronary BIfurcation Stenting) III bifurcation registries. Of 5,003 patients (93.8\%) with known baseline LVEF, 244 (4.9\%) had LVEF <40\% (bifurcation with reduced ejection fraction [BIFrEF] group), 430 (8.6\%) had LVEF 40\% to 49\% (bifurcation with mildly reduced ejection fraction [BIFmEF] group) and 4,329 (86.5\%) had ejection fraction (EF) >50\% (bifurcation with preserved ejection fraction [BIFpEF] group). The primary end point was the Kaplan-Meier estimate of major adverse cardiac events (MACEs) (a composite of all-cause death, myocardial infarction, and target vessel revascularization). Patients with BIFrEF had a more complex clinical profile and coronary anatomy. No difference in procedural (30 days) MACE was observed across EF categories, also after adjustment for in-study outcome predictors (BIFrEF vs BIFmEF: adjusted hazard ratio [adj-HR] 1.39, 95\% confidence interval [CI] 0.37 to 5.21, p = 0.626; BIFrEF vs BIFpEF: adj-HR 1.11, 95\% CI 0.25 to 2.87, p = 0.883; BIFmEF vs BIFpEF: adj-HR 0.81, 95\% CI 0.29 to 2.27, p = 0.683). BIFrEF was independently associated with long-term MACE (median follow-up 21 months, interquartile range 10 to 21 months) than both BIFmEF (adj-HR 2.20, 95\% CI 1.41 to 3.41, p <0.001) and BIFpEF (adj-HR 1.91, 95\% CI 1.41 to 2.60, p <0.001) groups, although no difference was observed between BIFmEF and BIFpEF groups (adj-HR 0.87, 95\% CI 0.61 to 1.24, p = 0.449). In conclusion, in patients who underwent PCI of a coronary bifurcation lesion according to contemporary clinical practice, reduced LVEF (<40\%), although a strong predictor of long-term MACEs, does not affect procedural outcomes.},
	author = {Gallone, Guglielmo and Kang, Jeehoon and Bruno, Francesco and Han, Jung-Kyu and De Filippo, Ovidio and Yang, Han-Mo and Doronzo, Mattia and Park, Kyung-Woo and Mittone, Gianluca and Kang, Hyun-Jae and Parma, Radoslaw and Gwon, Hyeon-Cheol and Cerrato, Enrico and Jung Chun, Woo and Smolka, Grzegorz and Hur, Seung-Ho and Helft, Gerard and Hwan Han, Seung and Muscoli, Saverio and Bin Song, Young and Figini, Filippo and Hong Choi, Ki and Boccuzzi, Giacomo and Hong, Soon-Jun and Trabattoni, Daniela and Nam, Chang-Wook and Giammaria, Massimo and Kim, Hyo-Soo and Conrotto, Federico and Escaned, Javier and Di Mario, Carlo and D'Ascenzo, Fabrizio and Koo, Bon-Kwon and de Ferrari, Gaetano Maria},
	date-modified = {2025-05-24 10:59:58 +0200},
	doi = {https://doi.org/10.1016/j.amjcard.2022.02.015},
	issn = {0002-9149},
	journal = {The American Journal of Cardiology},
	keywords = {ai, cardio},
	pages = {18--25},
	title = {Impact of Left Ventricular Ejection Fraction on Procedural and Long-Term Outcomes of Bifurcation Percutaneous Coronary Intervention},
	url = {https://www.sciencedirect.com/science/article/pii/S0002914922001692},
	volume = {172},
	year = {2022},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0002914922001692},
	bdsk-url-2 = {https://doi.org/10.1016/j.amjcard.2022.02.015}}

@article{DBLP:journals/information/PecoraroBB22,
  author       = {Roberto Pecoraro and
                  Valerio Basile and
                  Viviana Bono},
  title        = {Local Multi-Head Channel Self-Attention for Facial Expression Recognition},
  journal      = {Inf.},
  volume       = {13},
  number       = {9},
  pages        = {419},
  year         = {2022},
  url          = {https://doi.org/10.3390/info13090419},
  doi          = {10.3390/INFO13090419},
  timestamp    = {Tue, 18 Oct 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/information/PecoraroBB22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/rc/MalettoR22,
  author       = {Giacomo Maletto and
                  Luca Roversi},
  editor       = {Claudio Antares Mezzina and
                  Krzysztof Podlaski},
  title        = {Certifying Algorithms and Relevant Properties of Reversible Primitive
                  Permutations with Lean},
  booktitle    = {Reversible Computation - 14th International Conference, {RC} 2022,
                  Urbino, Italy, July 5-6, 2022, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {13354},
  pages        = {111--127},
  publisher    = {Springer},
  year         = {2022},
  url          = {https://doi.org/10.1007/978-3-031-09005-9\_8},
  doi          = {10.1007/978-3-031-09005-9\_8},
  timestamp    = {Mon, 25 Jul 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/rc/MalettoR22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/ercim/DamianiPR22,
  author       = {Ferruccio Damiani and
                  Luca Paolini and
                  Luca Roversi},
  title        = {Programming the Interaction with Quantum Coprocessors},
  journal      = {{ERCIM} News},
  volume       = {2022},
  number       = {128},
  year         = {2022},
  url          = {https://ercim-news.ercim.eu/en128/special/programming-the-interaction-with-quantum-coprocessors},
  timestamp    = {Thu, 06 Jan 2022 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/ercim/DamianiPR22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@phdthesis{DBLP:phd/basesearch/Antelmi22,
  author       = {Alessia Antelmi},
  title        = {Beyond Pairwise Relationships: Modeling Real-world Dynamics Via High-order
                  Networks},
  school       = {University of Salerno, Italy},
  year         = {2022},
  url          = {http://elea.unisa.it/xmlui/handle/10556/7005},
  timestamp    = {Wed, 14 Jan 2026 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/phd/basesearch/Antelmi22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/access/AntelmiDPSS22,
  author       = {Alessia Antelmi and
                  Giuseppe D'Ambrosio and
                  Andrea Petta and
                  Luigi Serra and
                  Carmine Spagnuolo},
  title        = {A Volunteer Computing Architecture for Computational Workflows on
                  Decentralized Web},
  journal      = {{IEEE} Access},
  volume       = {10},
  pages        = {98993--99010},
  year         = {2022},
  url          = {https://doi.org/10.1109/ACCESS.2022.3207167},
  doi          = {10.1109/ACCESS.2022.3207167},
  timestamp    = {Sun, 19 Jan 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/access/AntelmiDPSS22.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{GARCIA:PDP:21,
	abstract = {Stream Processing applications are spread across different sectors of industry and people's daily lives. The increasing data we produce, such as audio, video, image, and text are demanding quickly and efficiently computation. It can be done through Stream Parallelism, which is still a challenging task and most reserved for experts. We introduce a Stream Processing framework for assessing Parallel Programming Interfaces (PPIs). Our framework targets multi-core architectures and C++ stream processing applications, providing an API that abstracts the details of the stream operators of these applications. Therefore, users can easily identify all the basic operators and implement parallelism through different PPIs. In this paper, we present the proposed framework, implement three applications using its API, and show how it works, by using it to parallelize and evaluate the applications with the PPIs Intel TBB, FastFlow, and SPar. The performance results were consistent with the literature.},
	address = {Valladolid, Spain},
	author = {Garcia, Adriano Marques and Griebler, Dalvan and Schepke, Claudio and Fernandes, Luiz Gustavo},
	booktitle = {29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
	date-modified = {2024-08-18 14:42:47 +0200},
	doi = {10.1109/PDP52278.2021.00021},
	keywords = {parallel},
	month = mar,
	pages = {84--88},
	publisher = {IEEE},
	series = {PDP'21},
	title = {Introducing a Stream Processing Framework for Assessing Parallel Programming Interfaces},
	url = {https://iris.unito.it/retrieve/8aa73a3f-0b1f-41e4-9440-a87bbaf6e9c4/PDP_2021__Stream_bench_Framework_.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/8aa73a3f-0b1f-41e4-9440-a87bbaf6e9c4/PDP_2021__Stream_bench_Framework_.pdf}}

@article{21:journals:LaneseMM21,
	abstract = {The notion of reversible computing is attracting interest because of its applications in diverse fields, in particular the study of programming abstractions for fault tolerant systems. Most computational models are not naturally reversible since computation causes loss of information, and history information must be stored to enable reversibility. In the literature, two approaches to reverse the CCS process calculus exist, differing on how history information is kept. Reversible CCS (RCCS), proposed by Danos and Krivine, exploits dedicated stacks of memories attached to each thread. CCS with Keys (CCSK), proposed by Phillips and Ulidowski, makes CCS operators static so that computation does not cause information loss. In this paper we show that RCCS and CCSK are equivalent in terms of LTS isomorphism.},
	author = {Lanese, Ivan and Medi\'{c}, Doriana and Antares Mezzina, Claudio},
	doi = {10.1007/s00236-019-00346-6},
	journal = {Acta Informatica},
	keywords = {semantics},
	pages = {1--34},
	title = {Static versus dynamic reversibility in {CCS}},
	url = {https://doi.org/10.1007/s00236-019-00346-6},
	volume = {58},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1007/s00236-019-00346-6}}

@inproceedings{21:RC:AubertM21,
	abstract = {Existing formalisms for the algebraic specification and representation of networks of reversible agents suffer some shortcomings. Despite multiple attempts, reversible declensions of the Calculus of Communicating Systems (CCS) do not offer satisfactory adaptation of notions usual in ?forward-only? process algebras, such as replication or context. Existing formalisms disallow the ?hot-plugging? of processes during their execution in contexts with their own past. They also assume the existence of ?eternally fresh? keys or identifiers that, if implemented poorly, could result in unnecessary bottlenecks and look-ups involving all the threads. In this paper, we begin investigating those issues, by first designing a process algebra endowed with a mechanism to generate identifiers without the need to consult with the other threads. We use this calculus to recast the possible representations of non-determinism in CCS, and as a by-product establish a simple and straightforward definition of concurrency. Our reversible calculus is then proven to satisfy expected properties. We also observe that none of the reversible bisimulations defined thus far are congruences under our notion of ?reversible? contexts.},
	author = {Cl{\'{e}}ment Aubert and Doriana Medi\'{c}},
	booktitle = {Reversible Computation - 13th International Conference, {RC} 2021, Virtual Event, July 7-8, 2021, Proceedings},
	doi = {10.1007/978-3-030-79837-6\_9},
	keywords = {semantics},
	publisher = {Springer},
	title = {Explicit Identifiers and Contexts in Reversible Concurrent Calculus},
	url = {https://doi.org/10.1007/978-3-030-79837-6\_9},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1007/978-3-030-79837-6%5C_9}}

@inproceedings{21:DBLP:conf/embc/PinoVFCAS21,
	abstract = {Predicting response to treatment plays a key role to assist radiologists in hepato-cellular carcinoma (HCC) therapy planning. The most widely used treatment for unresectable HCC is the trans-arterial chemoembolization (TACE). A complete radiological response after the first TACE is a reliable predictor of treatment favourable outcome. However, visual inspection of contrast-enhanced CT scans is time-consuming, error prone and too operator-dependent. Thus, in this paper we propose TwinLiverNet: a deep neural network that is able to predict TACE treatment outcome through learning visual cue from CT scans. TwinLiverNet, specifically, integrates 3D convolutions and capsule networks and is designed to process simultaneously late arterial and delayed phases from contrast-enhanced CTs. Experimental results carried out on a dataset consisting of 126 HCC lesions show that TwinLiverNet reaches an average accuracy of 82\% in predicting complete response to TACE treatment. Furthermore, combining multiple CT phases (specifically, late arterial and delayed ones) yields a performance increase of over 12 percent points. Finally, the introduction of capsule layers into the model avoids the model to overfit, while enhancing accuracy.Clinical relevance--- TwinLiverNet supports radiologists in visual inspection of CT scans to assess TACE treatment outcome, while reducing inter-operator variability.},
	author = {Pino, Carmelo and Vecchio, Giuseppe and Fronda, Marco and Calandri, Marco and Aldinucci, Marco and Spampinato, Concetto},
	bibsource = {dblp computer science bibliography, https://dblp.org},
	biburl = {https://dblp.org/rec/conf/embc/PinoVFCAS21.bib},
	booktitle = {43rd Annual International Conference of the {IEEE} Engineering in Medicine {\&} Biology Society, {EMBC} 2021, Mexico, November 1-5, 2021},
	date-modified = {2024-04-01 11:36:45 +0200},
	doi = {10.1109/EMBC46164.2021.9630913},
	keywords = {ai},
	pages = {3039--3043},
	publisher = {{IEEE}},
	timestamp = {Thu, 06 Jan 2022 07:54:56 +0100},
	title = {TwinLiverNet: Predicting {TACE} Treatment Outcome from {CT} scans for Hepatocellular Carcinoma using Deep Capsule Networks},
	url = {https://doi.org/10.1109/EMBC46164.2021.9630913},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/EMBC46164.2021.9630913}}

@inproceedings{21:DSD:textarossa,
	abstract = {To achieve high performance and high energy efficiency on near-future exascale computing systems, three key technology gaps needs to be bridged. These gaps include: energy efficiency and thermal control; extreme computation efficiency via HW acceleration and new arithmetics; methods and tools for seamless integration of reconfigurable accelerators in heterogeneous HPC multi-node platforms. TEXTAROSSA aims at tackling this gap through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models and tools derived from European research.},
	address = {Palermo, Italy},
	author = {Agosta, Giovanni and Fornaciari, William and Galimberti, Andrea and Massari, Giuseppe and Reghenzani, Federico and Terraneo, Federico and Zoni, Davide and Brandolese, Carlo and Celino, Massimo and Iannone, Francesco and Palazzari, Paolo and Zummo, Giuseppe and Bernaschi, Massimo and D'Ambra, Pasqua and Saponara, Sergio and Danelutto, Marco and Torquati, Massimo and Aldinucci, Marco and Arfat, Yasir and Cantalupo, Barbara and Colonnelli, Iacopo and Esposito, Roberto and Martinelli, Alberto R. and Mittone, Gianluca and Beaumont, Olivier and Bramas, Berenger and Eyraud-Dubois, Lionel and Goglin, Brice and Guermouche, Abdou and Namyst, Raymond and Thibault, Samuel and Filgueras, Antonio and Vidal, Miquel and Alvarez, Carlos and Martorell, Xavier and Oleksiak, Ariel and Kulczewski, Michal and Lonardo, Alessandro and Vicini, Piero and Lo Cicero, Francesco and Simula, Francesco and Biagioni, Andrea and Cretaro, Paolo and Frezza, Ottorino and Stanislao Paolucci, Pier and Turisini, Matteo and Giacomini, Francesco and Boccali, Tommaso and Montangero, Simone and Ammendola, Roberto},
	booktitle = {Proc. of the 24th Euromicro Conference on Digital System Design ({DSD})},
	date-added = {2021-09-04 12:07:42 +0200},
	date-modified = {2021-09-04 12:23:41 +0200},
	doi = {10.1109/DSD53832.2021.00051},
	keywords = {textarossa, streamflow, hpc},
	month = aug,
	publisher = {IEEE},
	title = {{TEXTAROSSA}: Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/DSD53832.2021.00051}}

@inproceedings{21:laplace:enea,
	abstract = {This work exposes a practical methodology for the semi-automatic parallelization of existing code. We show how a scientific sequential code can be parallelized through our approach. The obtained parallel code is only slightly different from the starting sequential one, providing an example of how little re-designing our methodology involves. The performance of the parallelized code, executed on the CRESCO6 cluster, is then exposed and discussed. We also believe in the educational value of this approach and suggest its use as a teaching device for students.},
	author = {Aldinucci, Marco and Cesare, Valentina and Colonnelli, Iacopo and Martinelli, Alberto R. and Mittone, Gianluca and Cantalupo, Barbara},
	booktitle = {ENEA CRESCO in the fight against COVID-19},
	date-modified = {2025-05-24 09:40:06 +0200},
	editor = {Francesco Iannone},
	keywords = {hpc, hpc4ai},
	pages = {21--24},
	publisher = {ENEA},
	title = {Practical Parallelizazion of a {Laplace} Solver with {MPI}},
	year = {2021}}

@inproceedings{21:covi:enea,
	abstract = {HPC is an enabling platform for AI. The introduction of AI workloads in the HPC applications basket has non-trivial consequences both on the way of designing AI applications and on the way of providing HPC computing. This is the leitmotif of the convergence between HPC and AI. The formalized definition of AI pipelines is one of the milestones of HPC-AI convergence. If well conducted, it allows, on the one hand, to obtain portable and scalable applications. On the other hand, it is crucial for the reproducibility of scientific pipelines. In this work, we advocate the StreamFlow Workflow Management System as a crucial ingredient to define a parametric pipeline, called ``CLAIRE COVID-19 Universal Pipeline'', which is able to explore the optimization space of methods to classify COVID-19 lung lesions from CT scans, compare them for accuracy, and therefore set a performance baseline. The universal pipeline automatizes the training of many different Deep Neural Networks (DNNs) and many different hyperparameters. It, therefore, requires a massive computing power, which is found in traditional HPC infrastructure thanks to the portability-by-design of pipelines designed with StreamFlow. Using the universal pipeline, we identified a DNN reaching over 90\% accuracy in detecting COVID-19 lesions in CT scans.},
	author = {Colonnelli, Iacopo and Cantalupo, Barbara and Spampinato, Concetto and Pennisi, Matteo and Aldinucci, Marco},
	booktitle = {ENEA CRESCO in the fight against COVID-19},
	doi = {10.5281/zenodo.5151511},
	editor = {Francesco Iannone},
	keywords = {streamflow},
	publisher = {ENEA},
	title = {Bringing AI pipelines onto cloud-HPC: setting a baseline for accuracy of COVID-19 diagnosis},
	url = {https://iris.unito.it/retrieve/handle/2318/1796029/779853/21_AI-pipelines_ENEA-COVID19.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1796029/779853/21_AI-pipelines_ENEA-COVID19.pdf},
	bdsk-url-2 = {https://doi.org/10.5281/zenodo.5151511}}

@article{21:ajc:bifurcat,
	abstract = {Optimal dual antiplatelet therapy (DAPT) duration for patients undergoing percutaneous coronary intervention (PCI) for coronary bifurcations is an unmet issue. The BIFURCAT registry was obtained by merging two registries on coronary bifurcations. Three groups were compared in a two-by-two fashion: short-term DAPT (< 6 months), intermediate-term DAPT (6-12 months) and extended DAPT (>12 months). Major adverse cardiac events (MACE) (a composite of all-cause death, myocardial infarction (MI), target-lesion revascularization and stent thrombosis) were the primary endpoint. Single components of MACE were the secondary endpoints. Events were appraised according to the clinical presentation: chronic coronary syndrome (CCS) versus acute coronary syndrome (ACS). 5537 patients (3231 ACS, 2306 CCS) were included. After a median follow-up of 2.1 years (IQR 0.9-2.2), extended DAPT was associated with a lower incidence of MACE compared with intermediate-term DAPT (2.8\% versus 3.4\%, adjusted HR 0.23 [0.1-0.54], p <0.001), driven by a reduction of all-cause death in the ACS cohort. In the CCS cohort, an extended DAPT strategy was not associated with a reduced risk of MACE. In conclusion, among real-world patients receiving PCI for coronary bifurcation, an extended DAPT strategy was associated with a reduction of MACE in ACS but not in CCS patients.},
	author = {De Filippo, Ovidio and Kang, Jeehoon and Bruno, Francesco and Han, Jung-Kyu and Saglietto, Andrea and Yang, Han-Mo and Patti, Giuseppe and Park, Kyung-Woo and Parma, Radoslaw and Kim, Hyo-Soo and De Luca, Leonardo and Gwon, Hyeon-Cheol and Iannaccone, Mario and Jung Chun, Woo and Smolka, Grzegorz and Hur, Seung-Ho and Cerrato, Enrico and Hwan Han, Seung and Di Mario, Carlo and Bin Song, Young and Escaned, Javier and Hong Choi, Ki and Helft, Gerard and Doh, Joon-Hyung and Truffa Giachet, Alessandra and Hong, Soon-Jun and Muscoli, Saverio and Nam, Chang-Wook and Gallone, Guglielmo and Capodanno, Davide and Trabattoni, Daniela and Imori, Yoichi and Dusi, Veronica and Cortese, Bernardo and Montefusco, Antonio and Conrotto, Federico and Colonnelli, Iacopo and Sheiban, Imad and de Ferrari, Gaetano Maria and Koo, Bon-Kwon and D'Ascenzo, Fabrizio},
	date-modified = {2025-05-24 10:41:57 +0200},
	doi = {10.1016/j.amjcard.2021.07.005},
	issn = {0002-9149},
	journal = {The American Journal of Cardiology},
	keywords = {ai, cardio},
	title = {Benefit of Extended Dual Antiplatelet Therapy Duration in Acute Coronary Syndrome Patients Treated with Drug Eluting Stents for Coronary Bifurcation Lesions (from the {BIFURCAT} Registry)},
	url = {https://www.sciencedirect.com/science/article/pii/S0002914921006354},
	year = {2021},
	bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0002914921006354},
	bdsk-url-2 = {https://doi.org/10.1016/j.amjcard.2021.07.005}}

@article{21:ai4numbers:minerva,
	abstract = {This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.},
	author = {Arfat, Yasir and Mittone, Gianluca and Esposito, Roberto and Cantalupo, Barbara and de Ferrari, Gaetano Maria and Aldinucci, Marco},
	date-added = {2021-08-09 23:00:12 +0200},
	date-modified = {2021-08-09 23:05:36 +0200},
	doi = {10.23736/s2724-5683.21.05709-4},
	journal = {Minerva cardiology and angiology},
	keywords = {deephealth, hpc4ai, ai},
	title = {Machine Learning for Cardiology},
	url = {https://iris.unito.it/retrieve/handle/2318/1796298/780512/21_AI4numbers-preprint.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1796298/780512/21_AI4numbers-preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.23736/s2724-5683.21.05709-4}}

@article{21:jpdc:loop,
	abstract = {This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a little re-designing effort, turning an old codebase into \emph{modern} code, i.e., parallel and robust code. We propose a semi-automatic methodology to parallelize scientific applications designed with a purely sequential programming mindset, possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate that the same methodology works for the parallelization in the shared memory model (via OpenMP), message passing model (via MPI), and  General Purpose Computing on GPU model (via OpenACC). The method is demonstrated parallelizing four real-world sequential codes in the domain of physics and material science. The methodology itself has been distilled in collaboration with MSc students of the Parallel Computing course at the University of Torino, that applied it for the first time to the project works that they presented for the final exam of the course. Every year the course hosts some special lectures from industry representatives, who present how they use parallel computing and offer codes to be parallelizeda.},
	author = {Aldinucci, Marco and Cesare, Valentina and Colonnelli, Iacopo and Martinelli, Alberto R. and Mittone, Gianluca and Cantalupo, Barbara and Cavazzoni, Carlo and Drocco, Maurizio},
	date-added = {2021-06-10 22:05:54 +0200},
	date-modified = {2025-05-24 09:39:51 +0200},
	doi = {10.1016/j.jpdc.2021.05.017},
	journal = {Journal of Parallel and Distributed Computing},
	keywords = {HPC, parallel},
	pages = {13--29},
	title = {Practical Parallelization of Scientific Applications with {OpenMP, OpenACC and MPI}},
	url = {https://iris.unito.it/retrieve/handle/2318/1792557/770851/Practical_Parallelization_JPDC_preprint.pdf},
	volume = {157},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1792557/770851/Practical_Parallelization_JPDC_preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.jpdc.2021.05.017}}

@article{21:dagostino:lowpower,
	abstract = {Energy consumption is one of the major issues in today\&{\#}x2019;s computer science, and an increasing number of scientific communities are interested in evaluating the tradeoff between time-to-solution and energy-to-solution. Despite, in the last two decades, computing which revolved around centralized computing infrastructures, such as supercomputing and data centers, the wide adoption of the Internet of Things (IoT) paradigm is currently inverting this trend due to the huge amount of data it generates, pushing computing power back to places where the data are generated\&{\#}x2014;the so-called fog/edge computing. This shift towards a decentralized model requires an equivalent change in the software engineering paradigms, development environments, hardware tools, languages, and computation models for scientific programming because the local computational capabilities are typically limited and require a careful evaluation of power consumption. This paper aims to present how these concepts can be actually implemented in scientific software by presenting the state of the art of powerful, less power-hungry processors from one side and energy-aware tools and techniques from the other one.},
	author = {D'Agostino, Daniele and Merelli, Ivan and Aldinucci, Marco and Cesini, Daniele},
	da = {2021/06/09},
	date-added = {2021-06-10 22:03:27 +0200},
	date-modified = {2024-04-01 11:32:54 +0200},
	doi = {10.1155/2021/5514284},
	isbn = {1058-9244},
	journal = {Scientific Programming},
	keywords = {HPC},
	pages = {5514284},
	publisher = {Hindawi},
	title = {Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming},
	ty = {JOUR},
	url = {https://downloads.hindawi.com/journals/sp/2021/5514284.pdf},
	volume = {2021},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1155/2021/5514284}}

@article{21:neohic:bmc,
	abstract = {High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of DNA interactions and 3D chromosome folding at the genome-wide scale. Usually, these data are represented as matrices describing the binary contacts among the different chromosome regions. On the other hand, a graph-based representation can be advantageous to describe the complex topology achieved by the DNA in the nucleus of eukaryotic cells.},
	annote = {https://iris.unito.it/retrieve/handle/2318/1787302/756557/2021_neohic_BMC.pdf},
	author = {D'Agostino, Daniele and Li{\`o}, Pietro and Aldinucci, Marco and Merelli, Ivan},
	da = {2021/04/26},
	date-added = {2021-04-26 09:50:50 +0200},
	date-modified = {2021-04-26 15:42:52 +0200},
	doi = {10.1186/s12859-020-03937-0},
	isbn = {1471-2105},
	journal = {{BMC} Bioinformatics},
	keywords = {deephealth, hpc4ai},
	number = {2},
	pages = {43--58},
	title = {Advantages of using graph databases to explore chromatin conformation capture experiments},
	url = {https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-020-03937-0.pdf},
	volume = {22},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1186/s12859-020-03937-0}}

@incollection{21:bigdata:ius,
	abstract = {L'articolo affronta il tema dell'interoperabilit{\`a} dal punto di vista informatico, ponendo l'accento sulle infrastrutture necessarie affinch{\'e} la comunicazione tra sistemi informatici pubblici sia possibile. La struttura a silos su cui si basa il sistema informativo della pubblica amministrazione italiana risulta inadeguato all'approccio della big data analysis che, a contrario, richiede la piena comunicabilit{\`a} tra sistemi informativi affinch{\'e} il reperimento dei dati su cui condurre sperimentazioni sia quanto pi{\`u} facile e mirato.},
	author = {Marco Aldinucci},
	booktitle = {L'amministrazione pubblica con i big data: da Torino un dibattito sull'intelligenza artificiale},
	chapter = {15},
	editor = {Cavallo Perin, Roberto},
	isbn = {9788875901806},
	pages = {225--232},
	title = {L'infrastruttura necessaria per creare interoperabilit{\`a} tra pubbliche amministrazioni},
	url = {https://iris.unito.it/retrieve/handle/2318/1784335/748058/15.Aldinucci.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1784335/748058/15.Aldinucci.pdf}}

@article{21:eit:covidclaire,
	abstract = {A volunteer effort by Artificial Intelligence (AI) researchers has shown it can deliver significant research outcomes rapidly to help tackle COVID-19. Within two months, CLAIRE's self-organising volunteers delivered the World's first comprehensive curated repository of COVID-19-related datasets useful for drug-repurposing, drafted review papers on the role CT/X-ray scan analysis and robotics could play, and progressed research in other areas. Given the pace required and nature of voluntary efforts, the teams faced a number of challenges. These offer insights in how better to prepare for future volunteer scientific efforts and large scale, data-dependent AI collaborations in general. We offer seven recommendations on how to best leverage such efforts and collaborations in the context of managing future crises.},
	author = {Bontempi, Gianluca and Chavarriaga, Ricardo and De Canck, Hans and Girardi, Emanuela and Hoos, Holger and Kilbane{-}Dawe, Iarla and Ball, Tonio and Now{\'e}, Ann and Sousa, Jose and Bacciu, Davide and Aldinucci, Marco and De Domenico, Manlio and Saffiotti, Alessandro and Maratea, Marco},
	date-added = {2021-04-04 01:03:31 +0200},
	date-modified = {2025-05-24 10:45:37 +0200},
	doi = {10.1007/s10676-020-09567-7},
	journal = {Ethics and Information Technology},
	keywords = {deephealth},
	month = feb,
	publisher = {Springer},
	title = {The {CLAIRE COVID-19} initiative: approach, experiences and recommendations},
	url = {https://iris.unito.it/retrieve/handle/2318/1784271/747923/Bontempi2021_Article_TheCLAIRECOVID-19InitiativeApp-3.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1784271/747923/Bontempi2021_Article_TheCLAIRECOVID-19InitiativeApp-3.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s10676-020-09567-7}}

@inproceedings{21:CINI_acm_CF,
	abstract = {High-Performance Computing (HPC) is one of the strategic priorities for research and innovation worldwide due to its relevance for industrial and scientific applications. We envision HPC as composed of three pillars: infrastructures, applications, and key technologies and tools. While infrastructures are by construction centralized in large-scale HPC centers, and applications are generally within the purview of domain-specific organizations, key technologies fall in an intermediate case where coordination is needed, but design and development are often decentralized. A large group of Italian researchers has started a dedicated laboratory within the National Interuniversity Consortium for Informatics (CINI) to address this challenge. The laboratory, albeit young, has managed to succeed in its first attempts to propose a coordinated approach to HPC research within the EuroHPC Joint Undertaking, participating in the calls 2019-20 to five successful proposals for an aggregate total cost of 95M Euro. In this paper, we outline the working group's scope and goals and provide an overview of the five funded projects, which become fully operational in March 2021, and cover a selection of key technologies provided by the working group partners, highlighting their usage development within the projects.},
	address = {Virtual Conference, Italy},
	author = {Aldinucci, Marco and Agosta, Giovanni and Andreini, Antonio and Ardagna, Claudio A. and Bartolini, Andrea and Cilardo, Alessandro and Cosenza, Biagio and Danelutto, Marco and Esposito, Roberto and Fornaciari, William and Giorgi, Roberto and Lengani, Davide and Montella, Raffaele and Olivieri, Mauro and Saponara, Sergio and Simoni, Daniele and Torquati, Massimo},
	booktitle = {{ACM Computing Frontiers}},
	date-added = {2021-03-26 15:14:04 +0100},
	date-modified = {2024-03-26 14:27:56 +0100},
	doi = {10.1145/3457388.3458508},
	keywords = {admire, textarossa, eupex, eupilot},
	month = may,
	pages = {279--286},
	publisher = {ACM},
	title = {The {Italian} research on {HPC} key technologies across {EuroHPC}},
	url = {https://iris.unito.it/retrieve/handle/2318/1783118/744641/preprint.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1783118/744641/preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1145/3457388.3458508}}

@inproceedings{colonnelli_et_al:OASIcs.PARMA-DITAM.2021.5,
	abstract = {Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud Finding an effective way to improve accessibility to High-Performance Computing facilities, still anchored to SSH-based remote shells and queue-based job submission mechanisms, is an open problem in computer science. This work advocates a cloudification of HPC applications through a cluster-as-accelerator pattern, where computationally demanding portions of the main execution flow hosted on a Cloud infrastructure can be offloaded to HPC environments to speed them up. We introduce StreamFlow, a novel Workflow Management System that supports such a design pattern and makes it possible to run the steps of a standard workflow model on independent processing elements with no shared storage. We validated the proposed approach's effectiveness on the CLAIRE COVID-19 universal pipeline, i.e. a reproducible workflow capable of automating the comparison of (possibly all) state-of-the-art pipelines for the diagnosis of COVID-19 interstitial pneumonia from CT scans images based on Deep Neural Networks (DNNs).},
	address = {Dagstuhl, Germany},
	annote = {Projects: cloud computing, distributed computing, high-performance computing, streamflow, workflow management systems},
	author = {Colonnelli, Iacopo and Cantalupo, Barbara and Esposito, Roberto and Pennisi, Matteo and Spampinato, Concetto and Aldinucci, Marco},
	booktitle = {12th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures and 10th Workshop on Design Tools and Architectures for Multicore Embedded Computing Platforms (PARMA-DITAM 2021)},
	date-modified = {2025-05-24 09:12:41 +0200},
	doi = {10.4230/OASIcs.PARMA-DITAM.2021.5},
	editor = {Bispo, Jo\~{a}o and Cherubin, Stefano and Flich, Jos\'{e}},
	isbn = {978-3-95977-181-8},
	issn = {2190-6807},
	keywords = {system-sw, deephealth, hpc4ai, streamflow},
	pages = {5:1--5:13},
	publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
	series = {Open Access Series in Informatics (OASIcs)},
	title = {{HPC Application Cloudification: The StreamFlow Toolkit}},
	url = {https://drops.dagstuhl.de/opus/volltexte/2021/13641/pdf/OASIcs-PARMA-DITAM-2021-5.pdf},
	urn = {urn:nbn:de:0030-drops-136419},
	volume = {88},
	year = {2021},
	bdsk-url-1 = {https://drops.dagstuhl.de/opus/volltexte/2021/13641/pdf/OASIcs-PARMA-DITAM-2021-5.pdf},
	bdsk-url-2 = {https://doi.org/10.4230/OASIcs.PARMA-DITAM.2021.5}}

@article{21:lancet,
	abstract = {Background The accuracy of current prediction tools for ischaemic and bleeding events after an acute coronary syndrome (ACS) remains insufficient for individualised patient management strategies. We developed a machine learning-based risk stratification model to predict all-cause death, recurrent acute myocardial infarction, and major bleeding after ACS. Methods Different machine learning models for the prediction of 1-year post-discharge all-cause death, myocardial infarction, and major bleeding (defined as Bleeding Academic Research Consortium type 3 or 5) were trained on a cohort of 19826 adult patients with ACS (split into a training cohort [80%] and internal validation cohort [20%]) from the BleeMACS and RENAMI registries, which included patients across several continents. 25 clinical features routinely assessed at discharge were used to inform the models. The best-performing model for each study outcome (the PRAISE score) was tested in an external validation cohort of 3444 patients with ACS pooled from a randomised controlled trial and three prospective registries. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC). Findings The PRAISE score showed an AUC of 0.82 (95% CI 0.78-0.85) in the internal validation cohort and 0.92 (0.90-0.93) in the external validation cohort for 1-year all-cause death; an AUC of 0.74 (0.70-0.78) in the internal validation cohort and 0.81 (0.76-0.85) in the external validation cohort for 1-year myocardial infarction; and an AUC of 0.70 (0.66-0.75) in the internal validation cohort and 0.86 (0.82-0.89) in the external validation cohort for 1-year major bleeding. Interpretation A machine learning-based approach for the identification of predictors of events after an ACS is feasible and effective. The PRAISE score showed accurate discriminative capabilities for the prediction of all-cause death, myocardial infarction, and major bleeding, and might be useful to guide clinical decision making.},
	author = {D'Ascenzo, Fabrizio and De Filippo, Ovidio and Gallone, Guglielmo and Mittone, Gianluca and Deriu, Marco Agostino and Iannaccone, Mario and Ariza-Sol{\'e}, Albert and Liebetrau, Christoph and Manzano-Fern{\'a}ndez, Sergio and Quadri, Giorgio and Kinnaird, Tim and Campo, Gianluca and Simao Henriques, Jose Paulo and Hughes, James M. and Dominguez-Rodriguez, Alberto and Aldinucci, Marco and Morbiducci, Umberto and Patti, Giuseppe and Raposeiras-Roubin, Sergio and Abu-Assi, Emad and de Ferrari, Gaetano Maria and Piroli, Francesco and Saglietto, Andrea and Conrotto, Federico and Omed{\'e}, Pierluigi and Montefusco, Antonio and Pennone, Mauro and Bruno, Francesco and Bocchino, Pier Paolo and Boccuzzi, Giacomo and Cerrato, Enrico and Varbella, Ferdinando and Sperti, Michela and Wilton, Stephen B. and Velicki, Lazar and Xanthopoulou, Ioanna and Cequier, Angel and Iniguez-Romo, Andres and Munoz Pousa, Isabel and Cespon Fernandez, Maria and Caneiro Queija, Berenice and Cobas-Paz, Rafael and Lopez-Cuenca, Angel and Garay, Alberto and Flores Blanco, Pedro and Rognoni, Andrea and Biondi Zoccai, Giuseppe and Biscaglia, Simone and Nunez-Gil, Ivan and Fujii, Toshiharu and Durante, Alessandro and Song, Xiantao and Kawaji, Tetsuma and Alexopoulos, Dimitrios and Huczek, Zenon and Ramon Gonzalez Juanatey, Jose and Nie, Shao-Ping and Kawashiri, Masa-aki and Colonnelli, Iacopo and Cantalupo, Barbara and Esposito, Roberto and Leonardi, Sergio and Grosso Marra, Walter and Chieffo, Alaide and Michelucci, Umberto and Piga, Dario and Malavolta, Marta and Gili, Sebastiano and Mennuni, Marco and Montalto, Claudio and Oltrona Visconti, Luigi and Arfat, Yasir},
	date-modified = {2024-08-18 13:16:47 +0200},
	doi = {10.1016/S0140-6736(20)32519-8},
	issn = {0140-6736},
	journal = {The Lancet},
	keywords = {deephealth, hpc4ai, cardio, ai},
	number = {10270},
	pages = {199--207},
	title = {Machine learning-based prediction of adverse events following an acute coronary syndrome {(PRAISE)}: a modelling study of pooled datasets},
	url = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},
	volume = {397},
	year = {2021},
	bdsk-url-1 = {https://www.researchgate.net/profile/James_Hughes3/publication/348501148_Machine_learning-based_prediction_of_adverse_events_following_an_acute_coronary_syndrome_PRAISE_a_modelling_study_of_pooled_datasets/links/6002a81ba6fdccdcb858b6c2/Machine-learning-based-prediction-of-adverse-events-following-an-acute-coronary-syndrome-PRAISE-a-modelling-study-of-pooled-datasets.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/S0140-6736(20)32519-8}}

@misc{21:covid:aihub,
	abstract = {The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) taskforce on AI & COVID-19 supported the creation of a research group focused on AI-assisted diagnosis of COVID-19 pneumonia. The first results demonstrate the great potential of AI-assisted diagnostic imaging. Furthermore, the impact of the taskforce work is much larger, and it embraces the cross-fertilisation of artificial intelligence (AI) and high-performance computing (HPC): a partnership with rocketing potential for many scientific domains.},
	author = {Marco Aldinucci},
	date-modified = {2021-04-17 00:39:37 +0200},
	howpublished = {AIhub},
	keywords = {deephealth, hpc4ai},
	month = jan,
	note = {(magazine)},
	title = {High-performance computing and {AI} team up for {COVID-19} diagnostic imaging},
	url = {https://aihub.org/2021/01/12/high-performance-computing-and-ai-team-up-for-covid-19-diagnostic-imaging/},
	year = {2021},
	bdsk-url-1 = {https://aihub.org/2021/01/12/high-performance-computing-and-ai-team-up-for-covid-19-diagnostic-imaging/}}

@article{20Lstreamflow:tetc,
	abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single cell transcriptomic data analysis workflow.},
	author = {Iacopo Colonnelli and Barbara Cantalupo and Ivan Merelli and Marco Aldinucci},
	date-added = {2020-08-27 09:29:49 +0200},
	date-modified = {2020-08-27 09:36:33 +0200},
	doi = {10.1109/TETC.2020.3019202},
	journal = {{IEEE} {T}ransactions on {E}merging {T}opics in {C}omputing},
	keywords = {deephealth, hpc4ai, streamflow},
	number = {4},
	pages = {1723--1737},
	title = {{StreamFlow}: cross-breeding cloud with {HPC}},
	url = {https://arxiv.org/pdf/2002.01558},
	volume = {9},
	year = {2021},
	bdsk-url-1 = {https://arxiv.org/pdf/2002.01558},
	bdsk-url-2 = {https://doi.org/10.1109/TETC.2020.3019202}}

@inproceedings{21:ct:isbi,
	abstract = {Genetic profiling and characterization of lung cancers have recently emerged as a new technique for targeted therapeutic treatment based on immunotherapy or molecular drugs. However, the most effective way to discover specific gene mutations through tissue biopsy has several limitations, from invasiveness to being a risky procedure. Recently, quantitative assessment of visual features from CT data has been demonstrated to be a valid alternative to biopsy for the diagnosis of gene-addicted tumors. In this paper, we present a deep model for automated lesion segmentation and classification as gene-addicted or not. The segmentation approach extends the 2D Tiramisu architecture for 3D segmentation through dense blocks and squeeze-and-excitation layers, while a multi-scale 3D CNN is used for lesion classification. We also train our model with adversarial samples, and show that this approach acts as a gradient regularizer and enhances model interpretability. We also built a dataset, the first of its nature, consisting of 73 CT scans annotated with the presence of a specific genomics profile. We test our approach on this dataset achieving a segmentation accuracy of 93.11\% (Dice score) and a classification accuracy in identifying oncogene-addicted lung tumors of 82.00\%.},
	address = {Nice, France},
	author = {Carmelo Pino and Simone Palazzo and Francesca Trenta and Francesca Cordero and Ulas Bagci and Francesco Rundo and Sebastiano Battiato and Daniela Giordano and Marco Aldinucci and Concetto Spampinato},
	booktitle = {18th {IEEE} Intl. Symposium on Biomedical Imaging {(ISBI)}},
	date-modified = {2025-09-23 17:56:48 +0200},
	doi = {10.1109/ISBI48211.2021.9433832},
	keywords = {deephealth, ai},
	month = apr,
	publisher = {IEEE},
	title = {Interpretable Deep Model for Predicting Gene-Addicted Non-Small-Cell Lung Cancer in {CT} Scans},
	url = {https://iris.unito.it/retrieve/handle/2318/1790376/764762/21_ISBI_smallcell.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1790376/764762/21_ISBI_smallcell.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/ISBI48211.2021.9433832}}

@article{pennisi2021explainable,
	abstract = {COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes. Next, we combine the segmentation network with classification networks for COVID-19 identification and lesion categorization. We compare the model's classification results with those obtained by three expert radiologists on a dataset of 166 CT scans. Results showed a sensitivity of 90.3\% and a specificity of 93.5\% for COVID-19 detection, at least on par with those yielded by the expert radiologists, and an average lesion categorization accuracy of about 84\%. Moreover, a significant role is played by prior lung and lobe segmentation, that allowed us to enhance classification performance by over 6 percent points. The interpretation of the trained AI models reveals that the most significant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the artificial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http://perceivelab.com/covid-ai. The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decisions and that proactively involves them in the decision loop to further improve the COVID-19 understanding.},
	author = {Pennisi, Matteo and Kavasidis, Isaak and Spampinato, Concetto and Schinin{`{a}}, Vincenzo and Palazzo, Simone and Proietto Salanitri, Federica and Bellitto, Giovanni and Rundo, Francesco and Aldinucci, Marco and Cristofaro, Massimo and Campioni, Paolo and Pianura, Elisa and Di Stefano, Federica and Petrone, Ada and Albarello, Fabrizio and Ippolito, Giuseppe and Cuzzocrea, Salvatore and Conoci, Sabrina},
	date-modified = {2024-04-01 11:33:30 +0200},
	doi = {10.1016/j.artmed.2021.102114},
	journal = {Artificial Intelligence in Medicine},
	keynote = {deephealth},
	keywords = {ai},
	pages = {102114},
	publisher = {Elsevier},
	title = {An Explainable {AI} System for Automated {COVID-19} Assessment and Lesion Categorization from {CT-scans}},
	url = {https://iris.unito.it/retrieve/handle/2318/1792619/770952/2021_COVID_AIM_preprint.pdf},
	year = {2021},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1792619/770952/2021_COVID_AIM_preprint.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.artmed.2021.102114}}

@article{ZhaoBHRBMC21,
	abstract = {Classification algorithms have been widely adopted to detect anomalies for various systems, e.g., IoT, cloud and face recognition, under the common assumption that the data source is clean, i.e., features and labels are correctly set. However, data collected from the wild can be unreliable due to careless annotations or malicious data transformation for incorrect anomaly detection. In this article, we extend a two-layer on-line data selection framework: Robust Anomaly Detector (RAD) with a newly designed ensemble prediction where both layers contribute to the final anomaly detection decision. To adapt to the on-line nature of anomaly detection, we consider additional features of conflicting opinions of classifiers, repetitive cleaning, and oracle knowledge. We on-line learn from incoming data streams and continuously cleanse the data, so as to adapt to the increasing learning capacity from the larger accumulated data set. Moreover, we explore the concept of oracle learning that provides additional information of true labels for difficult data points. We specifically focus on three use cases, (i) detecting 10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big data jobs, and (iii) recognising 100 celebrities faces. Our evaluation results show that RAD can robustly improve the accuracy of anomaly detection, to reach up to 98.95 percent for IoT device attacks (i.e., +7%), up to 85.03 percent for cloud task failures (i.e., +14%) under 40 percent label noise, and for its extension, it can reach up to 77.51 percent for face recognition (i.e., +39%) under 30 percent label noise. The proposed RAD and its extensions are general and can be applied to different anomaly detection algorithms.},
	author = {Zhao, Zilong and Birke, Robert and Han, Rui and Robu, Bogdan and Bouchenak, Sara and Ben Mokhtar, Sonia and Chen, Lydia Y.},
	date-modified = {2024-04-01 11:20:03 +0200},
	doi = {10.1109/TDSC.2021.3063947},
	journal = {{IEEE} Trans. Dependable Secur. Comput.},
	keywords = {ai},
	number = {5},
	pages = {2177--2192},
	title = {Enhancing Robustness of On-Line Learning Models on Highly Noisy Data},
	url = {https://doi.org/10.1109/TDSC.2021.3063947},
	volume = {18},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/TDSC.2021.3063947}}

@article{BirkePQBC21,
	abstract = {Offering consistent low latency remains a key challenge for distributed applications, especially when deployed on the cloud where virtual machines (VMs) suffer from capacity variability caused by co-located tenants. Replicating redundant requests was shown to be an effective mechanism to defend application performance from high capacity variability. While the prior art centers on single-tier systems, it still remains an open question how to design replication strategies for distributed multi-tier systems. In this paper, we design a first of its kind PArtial REplication system, sPARE, that replicates and dispatches read-only workloads for distributed multi-tier web applications. The two key components of sPARE are (i) the variability-aware replicator that coordinates the replication levels on all tiers via an iterative searching algorithm, and (ii) the replication-aware arbiter that uses a novel token-based arbitration algorithm (TAD) to dispatch requests in each tier. We evaluate sPARE on web serving and searching applications, i.e., MediaWiki and Solr, the former deployed on our private cloud and the latter on Amazon EC2. Our results based on various interference patterns and traffic loads show that sPARE is able to improve the tail latency of MediaWiki and Solr by a factor of almost 2.7x and 2.9x, respectively.},
	author = {Birke, Robert and P{\'{e}}rez, Juan F. and Qiu, Zhan and Bj{"{o}}rkqvist, Mathias and Chen, Lydia Y.},
	date-modified = {2024-04-01 11:31:37 +0200},
	doi = {10.1109/TSC.2017.2780845},
	journal = {{IEEE} Trans. Serv. Comput.},
	keywords = {parallel},
	number = {2},
	pages = {574--588},
	title = {sPARE: Partial Replication for Multi-Tier Applications in the Cloud},
	url = {https://doi.org/10.1109/TSC.2017.2780845},
	volume = {14},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/TSC.2017.2780845}}

@inproceedings{pmlr-v157-zhao21a,
	abstract = {While data sharing is crucial for knowledge development, privacy concerns and strict regulation (e.g., European General Data Protection Regulation (GDPR)) unfortunately limit its full effectiveness. Synthetic tabular data emerges as an alternative to enable data sharing while fulfilling regulatory and privacy constraints. The state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GAN) and address two main data types in industry, i.e., continuous and categorical. In this paper, we develop CTAB-GAN, a novel conditional table GAN architecture that can effectively model diverse data types, including a mix of continuous and categorical variables. Moreover, we address data imbalance and long tail issues, i.e., certain variables have drastic frequency differences across large values. To achieve those aims, we first introduce the information loss, classification loss and generator loss to the conditional GAN. Secondly, we design a novel conditional vector, which efficiently encodes the mixed data type and skewed distribution of data variable. We extensively evaluate CTAB-GAN with the state of the art GANs that generate synthetic tables, in terms of data similarity and analysis utility. The results on five datasets show that the synthetic data of CTAB-GAN remarkably resembles the real data for all three types of variables and results into higher accuracy for five machine learning algorithms, by up to 17%.},
	author = {Zhao, Zilong and Kunar, Aditya and Birke, Robert and Chen, Lydia Y.},
	booktitle = {Proceedings of The 13th Asian Conference on Machine Learning},
	date-modified = {2025-05-24 09:21:39 +0200},
	editor = {Balasubramanian, Vineeth N. and Tsang, Ivor},
	keywords = {ai},
	month = nov,
	pages = {97--112},
	pdf = {https://proceedings.mlr.press/v157/zhao21a/zhao21a.pdf},
	publisher = {PMLR},
	series = {Proceedings of Machine Learning Research},
	title = {CTAB-GAN: Effective Table Data Synthesizing},
	url = {https://proceedings.mlr.press/v157/zhao21a.html},
	volume = {157},
	year = {2021},
	bdsk-url-1 = {https://proceedings.mlr.press/v157/zhao21a.html}}

@inproceedings{pmlr-v157-younesian21a,
	abstract = {Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media from non-experts. Active querying experts are conventionally adopted to provide labels for the informative samples which don't have labels, instead of possibly incorrect labels. The new challenge that arises here is how to discern the informative and noisy labels which benefit from expert cleaning.  In this paper, we aim to leverage the stringent oracle budget to robustly maximize learning accuracy. We propose a noise-aware active learning framework, QActor, and a novel measure \emph{CENT}, which considers both cross-entropy and entropy to select informative and noisy labels for an expert cleansing. QActor iteratively cleans samples via quality models and actively querying an expert on those noisy yet informative samples. To adapt to learning capacity per iteration, QActor dynamically adjusts the query limit according to the learning loss for each learning iteration. We extensively evaluate different image datasets with noise label ratios ranging between 30% and 60%. Our results show that QActor can nearly match the optimal accuracy achieved using only clean data at the cost of only an additional 10% of ground truth data from the oracle.},
	author = {Younesian, Taraneh and Zhao, Zilong and Ghiassi, Amirmasoud and Birke, Robert and Chen, Lydia Y.},
	booktitle = {Proceedings of The 13th Asian Conference on Machine Learning},
	date-modified = {2025-05-24 09:19:25 +0200},
	editor = {Balasubramanian, Vineeth N. and Tsang, Ivor},
	keywords = {ai},
	month = nov,
	pages = {548--563},
	pdf = {https://proceedings.mlr.press/v157/younesian21a/younesian21a.pdf},
	publisher = {PMLR},
	series = {Proceedings of Machine Learning Research},
	title = {{QActor}: Active Learning on Noisy Labels},
	url = {https://proceedings.mlr.press/v157/younesian21a.html},
	volume = {157},
	year = {2021},
	bdsk-url-1 = {https://proceedings.mlr.press/v157/younesian21a.html}}

@inproceedings{bdcat-ghiassi21,
	abstract = {Big Data systems allow collecting massive datasets to feed the data hungry deep learning. Labelling these ever-bigger datasets is increasingly challenging and label errors affect even highly curated sets. This makes robustness to label noise a critical property for weakly-supervised classifiers. The related works on resilient deep networks tend to focus on a limited set of synthetic noise patterns, and with disparate views on their impacts, e.g., robustness against symmetric v.s. asymmetric noise patterns. In this paper, we first extend the theoretical analysis of test accuracy for any given noise patterns. Based on the insights, we design TrustNet that first learns the pattern of noise corruption, being it both symmetric or asymmetric, from a small set of trusted data. Then, TrustNet is trained via a robust loss function, which weights the given labels against the inferred labels from the learned noise pattern. The weight is adjusted based on model uncertainty across training epochs. We evaluate TrustNet on synthetic label noise for CIFAR-10, CIFAR-100 and big real-world data with label noise, i.e., Clothing1M. We compare against state-of-the-art methods demonstrating the strong robustness of TrustNet under a diverse set of noise patterns.},
	author = {Ghiassi, Amirmasoud and Birke, Robert and Chen, Lydia Y.},
	booktitle = {8th {IEEE/ACM} International Conference on Big Data Computing, Applications and Technologies ({BDCAT})},
	date-modified = {2025-05-24 09:18:42 +0200},
	doi = {10.1145/3492324.3494166},
	keywords = {ai},
	month = dec,
	pages = {52--62},
	publisher = {{ACM}},
	title = {{TrustNet}: Learning from Trusted Data Against (A)symmetric Label Noise},
	url = {https://doi.org/10.1145/3492324.3494166},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1145/3492324.3494166}}

@inproceedings{etfa-albanese21,
	abstract = {Enterprises in the field of industrial automation experience an increasing demand for providing virtualized software solutions. Inspired by the recent trends in serverless and cloud computing, software virtualization is considered even for safety-critical applications with hard real-time requirements, as a means of avoiding hardware vendor lock-in and reducing volume and maintenance cost of devices. In this work, we evaluate the applicability of OS-level virtualization to an industrial automation use case. Our application runs in Docker containers on top of Linux patched with PREEMPT_RT. We investigate the ability of Docker coupled with diverse networking technologies to fulfill the latency requirements of the application under normal or heavy system load. We empirically compare four networking technologies with respect to communication latency and frequency of missing packets. The results indicate that Docker with certain technologies, such as the Single Root I/O Virtualization interface, performs robustly even under heavy load, enabling sufficient performance isolation and low overhead that does not jeopardise the real-time performance of our application.},
	author = {Giuliano Albanese and Robert Birke and Georgia Giannopoulou and Sandro Sch{\"{o}}nborn and Thanikesavan Sivanthi},
	booktitle = {26th {IEEE} International Conference on Emerging Technologies and Factory Automation ({ETFA})},
	date-modified = {2025-05-24 09:38:18 +0200},
	doi = {10.1109/ETFA45728.2021.9613320},
	editor = {Chiozzi, Gianluca and Ibsen, Jorge},
	month = sep,
	pages = {1--8},
	publisher = {{IEEE}},
	title = {Evaluation of Networking Options for Containerized Deployment of Real-Time Applications},
	url = {https://doi.org/10.1109/ETFA45728.2021.9613320},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/ETFA45728.2021.9613320}}

@inproceedings{ijcnn-ghiassi21,
	abstract = {Today's available datasets in the wild, e.g., from social media and open platforms, present tremendous opportunities and challenges for deep learning, as there is a significant portion of tagged images, but often with noisy, i.e. erroneous, labels. Recent studies improve the robustness of deep models against noisy labels without the knowledge of true labels. In this paper, we advocate to derive a stronger classifier which proactively makes use of the noisy labels in addition to the original images - turning noisy labels into learning features. To such an end, we propose a novel framework, LABELNET, composed of Amateur and Expert, which iteratively learn from each other. Amateur is a regular image classifier trained by the feedback of Expert, which imitates how human experts would correct the predicted labels from Amateur using the noise pattern learnt from the knowledge of both the noisy and ground truth labels. The trained Amateur and Expert proactively leverage the images and their noisy labels to infer image classes. Our empirical evaluations on noisy versions of MNIST, CIFAR-10, CIFAR-100 and real-world data of Clothing1M show that the proposed model can achieve robust classification against a wide range of noise ratios and with as little as 20-50% training data, compared to state-of-the-art deep models that solely focus on distilling the impact of noisy labels.},
	author = {Ghiassi, Amirmasoud and Birke, Robert and Han, Rui and Chen, Lydia Y.},
	booktitle = {International Joint Conference on Neural Networks ({IJCNN})},
	date-modified = {2025-05-24 09:19:39 +0200},
	doi = {10.1109/IJCNN52387.2021.9533562},
	keywords = {ai},
	month = jul,
	pages = {1--8},
	publisher = {{IEEE}},
	title = {{LABELNET:} Recovering Noisy Labels},
	url = {https://doi.org/10.1109/IJCNN52387.2021.9533562},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/IJCNN52387.2021.9533562}}

@inproceedings{percom-cox21a,
	abstract = {Deep neural networks (DNNs) are becoming the core components of many applications running on edge devices, especially for real time image-based analysis. Increasingly, multi-faced knowledge is extracted via executing multiple DNNs inference models, e.g., identifying objects, faces, and genders from images. The response times of multi-DNN highly affect users' quality of experience and safety as well. Different DNNs exhibit diversified resource requirements and execution patterns across layers and networks, which may easily exceed the available device memory and riskily degrade the responsiveness. In this paper, we design and implement Masa, a responsive memory-aware multi-DNN execution framework, an on-device middleware featuring on modeling inter- and intra-network dependency and leveraging complimentary memory usage of each layer. Masa can consistently ensure the average response time when deterministically and stochastically executing multiple DNN-based image analyses. We extensively evaluate Masa on three configurations of Raspberry Pi and a large set of popular DNN models triggered by different generation patterns of images. Our evaluation results show that Masa can achieve lower average response times by up to 90% on devices with small memory, i.e., 512 MB to 1 GB, compared to the state of the art multi-DNN scheduling solutions.},
	author = {Cox, Bart and Galjaard, Jeroen and Ghiassi, Amirmasoud and Birke, Robert and Chen, Lydia Y.},
	booktitle = {19th {IEEE} International Conference on Pervasive Computing and Communications ({PerCom})},
	doi = {10.1109/PERCOM50583.2021.9439111},
	month = mar,
	pages = {1--10},
	publisher = {{IEEE}},
	title = {Masa: Responsive Multi-DNN Inference on the Edge},
	url = {https://doi.org/10.1109/PERCOM50583.2021.9439111},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/PERCOM50583.2021.9439111}}

@inproceedings{percom-galjaard21,
	abstract = {The execution of deep neural network (DNN) inference jobs on edge devices has become increasingly popular. Multiple of such inference models can concurrently analyse the on-device data, e.g. images, to extract valuable insights. Prior art focuses on low-power accelerators, compressed neural network architectures, and specialized frameworks to reduce execution time of single inference jobs on edge devices which are resource constrained. However, it is little known how different scheduling policies can further improve the runtime performance of multi-inference jobs without additional edge resources. To enable the exploration of scheduling policies, we first develop an execution framework, EdgeCaffe, which splits the DNN inference jobs by loading and execution of each network layer. We empirically characterize the impact of loading and scheduling policies on the execution time of multi-inference jobs and point out their dependency on the available memory space. We propose a novel memory-aware scheduling policy, MemA, which opportunistically interleaves the executions of different types of DNN layers based on their estimated run-time memory demands. Our evaluation on exhaustive combinations of five networks, data inputs, and memory configurations show that MemA can alleviate the degradation of execution times of multi-inference (up to 5x) under severely constrained memory compared to standard scheduling policies without affecting accuracy.},
	author = {Galjaard, Jeroen and Cox, Bart and Ghiassi, Amirmasoud and Chen, Lydia Y. and Birke, Robert},
	booktitle = {19th {IEEE} International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events},
	date-modified = {2025-05-24 10:46:04 +0200},
	doi = {10.1109/PerComWorkshops51409.2021.9430952},
	keywords = {ai},
	month = mar,
	pages = {281--286},
	publisher = {{IEEE}},
	title = {{MemA}: Fast Inference of Multiple Deep Models},
	url = {https://doi.org/10.1109/PerComWorkshops51409.2021.9430952},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1109/PerComWorkshops51409.2021.9430952}}

@inproceedings{www-hong21,
	abstract = {Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregating results from crowd workers. To ensure the time relevance and overcome slow responses of workers, online label aggregation is increasingly requested, calling for solutions that can incrementally infer true label distribution via subsets of data items. In this paper, we propose a novel online label aggregation framework, BiLA , which employs variational Bayesian inference method and designs a novel stochastic optimization scheme for incremental training. BiLA is flexible to accommodate any generating distribution of labels by the exact computation of its posterior distribution. We also derive the convergence bound of the proposed optimizer. We compare BiLA with the state of the art based on minimax entropy, neural networks and expectation maximization algorithms, on synthetic and real-world data sets. Our evaluation results on various online scenarios show that BiLA can effectively infer the true labels, with an error rate reduction of at least 10 to 1.5 percent points for synthetic and real-world datasets, respectively.},
	author = {Chi Hong and Amirmasoud Ghiassi and Yichi Zhou and Robert Birke and Lydia Y. Chen},
	booktitle = {{WWW} '21: The Web Conference 2021},
	date-modified = {2024-04-01 11:20:18 +0200},
	doi = {10.1145/3442381.3449933},
	editor = {Jure Leskovec and Marko Grobelnik and Marc Najork and Jie Tang and Leila Zia},
	keywords = {ai},
	month = apr,
	pages = {1904--1915},
	publisher = {{ACM} / {IW3C2}},
	title = {Online Label Aggregation: {A} Variational Bayesian Approach},
	url = {https://doi.org/10.1145/3442381.3449933},
	year = {2021},
	bdsk-url-1 = {https://doi.org/10.1145/3442381.3449933}}

@inproceedings{DBLP:conf/rc/MatosPR21,
  author       = {Armando B. Matos and
                  Luca Paolini and
                  Luca Roversi},
  editor       = {Shigeru Yamashita and
                  Tetsuo Yokoyama},
  title        = {Splitting Recursion Schemes into Reversible and Classical Interacting
                  Threads},
  booktitle    = {Reversible Computation - 13th International Conference, {RC} 2021,
                  Virtual Event, July 7-8, 2021, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {12805},
  pages        = {204--213},
  publisher    = {Springer},
  year         = {2021},
  url          = {https://doi.org/10.1007/978-3-030-79837-6\_12},
  doi          = {10.1007/978-3-030-79837-6\_12},
  timestamp    = {Tue, 13 Jul 2021 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/rc/MatosPR21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2102-09436,
  author       = {Armando B. Matos and
                  Luca Paolini and
                  Luca Roversi},
  title        = {Interleaving classical and reversible},
  journal      = {CoRR},
  volume       = {abs/2102.09436},
  year         = {2021},
  url          = {https://arxiv.org/abs/2102.09436},
  eprinttype   = {arXiv},
  eprint       = {2102.09436},
  timestamp    = {Wed, 24 Feb 2021 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2102-09436.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/access/AntelmiCSS21,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Vittorio Scarano and
                  Carmine Spagnuolo},
  title        = {Modeling and Evaluating Epidemic Control Strategies With High-Order
                  Temporal Networks},
  journal      = {{IEEE} Access},
  volume       = {9},
  pages        = {140938--140964},
  year         = {2021},
  url          = {https://doi.org/10.1109/ACCESS.2021.3119459},
  doi          = {10.1109/ACCESS.2021.3119459},
  timestamp    = {Tue, 07 May 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/access/AntelmiCSS21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/entropy/AntelmiCSS21,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Carmine Spagnuolo and
                  Przemyslaw Szufel},
  title        = {Social Influence Maximization in Hypergraphs},
  journal      = {Entropy},
  volume       = {23},
  number       = {7},
  pages        = {796},
  year         = {2021},
  url          = {https://doi.org/10.3390/e23070796},
  doi          = {10.3390/E23070796},
  timestamp    = {Mon, 05 Feb 2024 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/entropy/AntelmiCSS21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/cig/LoriaAP21,
  author       = {Enrica Loria and
                  Alessia Antelmi and
                  Johanna Pirker},
  title        = {Comparing the Structures and Characteristics of Different Game Social
                  Networks - The Steam Case},
  booktitle    = {2021 {IEEE} Conference on Games (CoG), Copenhagen, Denmark, August
                  17-20, 2021},
  pages        = {1--8},
  publisher    = {{IEEE}},
  year         = {2021},
  url          = {https://doi.org/10.1109/CoG52621.2021.9619130},
  doi          = {10.1109/COG52621.2021.9619130},
  timestamp    = {Tue, 01 Apr 2025 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/cig/LoriaAP21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{DBLP:data/10/AntelmiCSS21,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Carmine Spagnuolo and
                  Przemyslaw Szufel},
  title        = {Social Influence Maximization in Hypergraphs - Data set (Version 0.0.1)},
  publisher    = {Zenodo},
  year         = {2021},
  month        = jun,
  howpublished = {\url{https://doi.org/10.5281/zenodo.4913158}},
  note         = {Accessed on YYYY-MM-DD.},
  url          = {https://doi.org/10.5281/zenodo.4913158},
  doi          = {10.5281/ZENODO.4913158},
  timestamp    = {Tue, 01 Oct 2024 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/data/10/AntelmiCSS21.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{DBLP:data/10/AntelmiCSS21a,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Carmine Spagnuolo and
                  Przemyslaw Szufel},
  title        = {Social Influence Maximization in Hypergraphs - Julia Package (Version
                  0.0.1)},
  publisher    = {Zenodo},
  year         = {2021},
  month        = jun,
  howpublished = {\url{https://doi.org/10.5281/zenodo.4915973}},
  note         = {Accessed on YYYY-MM-DD.},
  url          = {https://doi.org/10.5281/zenodo.4915973},
  doi          = {10.5281/ZENODO.4915973},
  timestamp    = {Thu, 12 Dec 2024 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/data/10/AntelmiCSS21a.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@misc{DBLP:data/10/AntelmiCSS21b,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Carmine Spagnuolo and
                  Przemyslaw Szufel},
  title        = {Social Influence Maximization in Hypergraphs - Julia Package (Version
                  0.0.2)},
  publisher    = {Zenodo},
  year         = {2021},
  month        = jun,
  howpublished = {\url{https://doi.org/10.5281/zenodo.4916324}},
  note         = {Accessed on YYYY-MM-DD.},
  url          = {https://doi.org/10.5281/zenodo.4916324},
  doi          = {10.5281/ZENODO.4916324},
  timestamp    = {Thu, 12 Dec 2024 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/data/10/AntelmiCSS21b.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{GARCIA:ICCSA:20,
	abstract = {Since the demand for computing power increases, new architectures emerged to obtain better performance. Reducing the power and energy consumption of these architectures is one of the main challenges to achieving high-performance computing. Current research trends aim at developing new software and hardware techniques to achieve the best performance and energy trade-offs. In this work, we investigate the impact of different CPU frequency scaling techniques such as ondemand, performance, and powersave on the power and energy consumption of multi-core based computer infrastructure. We apply these techniques in PAMPAR, a parallel benchmark suite implemented in PThreads, OpenMP, MPI-1, and MPI-2 (spawn). We measure the energy and execution time of 10 benchmarks, varying the number of threads. Our results show that although powersave consumes up to 43.1\% less power than performance and ondemand governors, it consumes the triple of energy due to the high execution time. Our experiments also show that the performance governor consumes up to 9.8\% more energy than ondemand for CPU-bound benchmarks. Finally, our results show that PThreads has the lowest power consumption, consuming less than the sequential version for memory-bound benchmarks. Regarding performance, the performance governor achieved 3\% of performance over the ondemand.},
	address = {Cagliari, Italy},
	author = {Garcia, Adriano Marques and Serpa, Matheus and Griebler, Dalvan and Schepke, Claudio and Fernandes, Luiz Gustavo and Navaux, Philippe O. A.},
	booktitle = {International Conference on Computational Science and its Applications (ICCSA)},
	date-modified = {2024-08-18 14:42:47 +0200},
	doi = {10.1007/978-3-030-58817-5_12},
	keywords = {parallel},
	month = jul,
	pages = {142--157},
	publisher = {Springer},
	series = {ICCSA'20},
	title = {The Impact of CPU Frequency Scaling on Power Consumption of Computing Infrastructures},
	url = {https://iris.unito.it/retrieve/3b8f3dc0-cd4d-4f36-801d-9e8c613ea2e8/ICCSA_Energy_governors_preprint.pdf},
	volume = {12254},
	year = {2020},
	bdsk-url-1 = {https://iris.unito.it/retrieve/3b8f3dc0-cd4d-4f36-801d-9e8c613ea2e8/ICCSA_Energy_governors_preprint.pdf}}

@article{20:journals:MedicMPY20,
	abstract = {This paper presents a study of causality in a reversible, concurrent setting. There exist various notions of causality in Pi-calculus, which differ in the treatment of parallel extrusions of the same name. Hence, by using a parametric way of bookkeeping the order and the dependencies among extruders it is possible to map different causal semantics into the same framework. Starting from this simple observation, we present a uniform framework for reversible ?-calculi that is parametric with respect to a data structure that stores information about the extrusion of a name. Different data structures yield different approaches to the parallel extrusion problem. We map three well-known causal semantics into our framework. We prove causal-consistency for the three instances of our framework. Furthermore, we prove a causal correspondence between the appropriate instances of the framework and the Boreale-Sangiorgi semantics and an operational correspondence with the reversible \emph{{\(\pi\)}}-calculus causal semantics.},
	author = {Doriana Medi\'{c} and Claudio Antares Mezzina and Iain Phillips and Nobuko Yoshida},
	doi = {10.1016/j.ic.2020.104644},
	journal = {Information and Computation},
	keywords = {, semantics},
	pages = {104644},
	title = {A parametric framework for reversible \emph{{\(\pi\)}}-calculi},
	url = {https://doi.org/10.1016/j.ic.2020.104644},
	volume = {275},
	year = {2020},
	bdsk-url-1 = {https://doi.org/10.1016/j.ic.2020.104644}}

@inproceedings{20:concur:LaneseM20,
	abstract = {Reversible computing is a paradigm where programs can execute backward as well as in the usual forward direction. Reversible computing is attracting interest due to its applications in areas as different as biochemical modelling, simulation, robotics and debugging, among others. In concurrent systems the main notion of reversible computing is called causal-consistent reversibility, and it allows one to undo an action if and only if its consequences, if any, have already been undone. This paper presents a general and automatic technique to define a causal-consistent reversible extension for given forward models. We support models defined using a reduction semantics in a specific format and consider a causality relation based on resources consumed and produced. The considered format is general enough to fit many formalisms studied in the literature on causal-consistent reversibility, notably Higher-Order ?-calculus and Core Erlang, an intermediate language in the Erlang compilation. Reversible extensions of these models in the literature are ad hoc, while we build them using the same general technique. This also allows us to show in a uniform way that a number of relevant properties, causal-consistency in particular, hold in the reversible extensions we build. Our technique also allows us to go beyond the reversible models in the literature: we cover a larger fragment of Core Erlang, including remote error handling based on links, which has never been considered in the reversibility literature.},
	author = {Ivan Lanese and Doriana Medi\'{c}},
	booktitle = {31st International Conference on Concurrency Theory, {CONCUR} 2020, September 1-4, 2020, Vienna, Austria (Virtual Conference)},
	doi = {10.4230/LIPIcs.CONCUR.2020.33},
	keywords = {, semantics},
	pages = {33:1--33:24},
	publisher = {Schloss Dagstuhl - Leibniz-Zentrum f{\"{u}}r Informatik},
	series = {LIPIcs},
	title = {A General Approach to Derive Uncontrolled Reversible Semantics},
	url = {https://doi.org/10.4230/LIPIcs.CONCUR.2020.33},
	volume = {171},
	year = {2020},
	bdsk-url-1 = {https://doi.org/10.4230/LIPIcs.CONCUR.2020.33}}

@inproceedings{20:RC:MedicM0Y20,
	abstract = {Software transactional memory (STM) is a concurrency control mechanism for shared memory systems. It is opposite to the lock based mechanism, as it allows multiple processes to access the same set of variables in a concurrent way. Then according to the used policy, the effect of accessing to shared variables can be committed (hence, made permanent) or undone. In this paper, we define a formal framework for describing STMs and show how with a minor variation of the rules it is possible to model two common policies for STM: reader preference and writer preference.},
	author = {Doriana Medi\'{c} and Claudio Antares Mezzina and Iain Phillips and Nobuko Yoshida},
	booktitle = {Reversible Computation - 12th International Conference, {RC} 2020, Oslo, Norway, July 9-10, 2020, Proceedings},
	doi = {10.1007/978-3-030-52482-1\_16},
	keywords = {semantics},
	pages = {255--263},
	publisher = {Springer},
	series = {Lecture Notes in Computer Science},
	title = {Towards a Formal Account for Software Transactional Memory},
	url = {https://doi.org/10.1007/978-3-030-52482-1_16},
	volume = {12227},
	year = {2020},
	bdsk-url-1 = {https://doi.org/10.1007/978-3-030-52482-1_16}}

@misc{20:covid:ag,
	abstract = {La Task Force su AI&COVID-19 della confederazione europea dei laboratori di ricerca sull'intelligenza artificiale (CLAIRE) ha sostenuto la creazione di un gruppo di ricerca focalizzato sulla diagnosi della polmonite da COVID assistita dall'Intelligenza Artificiale. I primi risultati sono incoraggianti},
	author = {Marco Aldinucci},
	date-modified = {2021-04-17 00:42:11 +0200},
	howpublished = {Agenda Digitale},
	keywords = {deephealth, hpc4ai},
	month = nov,
	note = {(magazine)},
	title = {Polmonite da {COVID-19}, diagnosi con l'intelligenza artificiale: Italia in prima fila},
	url = {https://www.agendadigitale.eu/sanita/polmonite-da-covid-19-allo-studio-la-diagnosi-tramite-intelligenza-artificiale-italia-in-prima-fila/},
	year = {2020},
	bdsk-url-1 = {https://www.agendadigitale.eu/sanita/polmonite-da-covid-19-allo-studio-la-diagnosi-tramite-intelligenza-artificiale-italia-in-prima-fila/}}

@inproceedings{20:farm:rtas,
	abstract = {High throughput applications with real-time guar- antees are increasingly relevant. For these applications, parallelism must be exposed to meet deadlines. Directed Acyclic Graphs (DAGs) are a popular and very general application model that can capture any possible interaction among threads. However, we argue that by constraining the application structure to a set of composable ``skeletons'', at the price of losing some generality w.r.t. DAGs, the following advantages are gained: (i) a finer model of the application enables tighter analysis, (ii) specialised scheduling policies are applicable, (iii) programming is simplified, (iv) specialised implementation techniques can be exploited transparently, and (v) the program can be automatically tuned to minimise resource usage while still meeting its hard deadlines. As a first step towards a set of real-time skeletons we conduct a case study with the job farm skeleton and the hard real- time XMOS xCore-200 microcontroller. We present an analytical framework for job farms that reduces the number of required cores by scheduling jobs in batches, while ensuring that deadlines are still met. Our experimental results demonstrate that batching reduces the minimum sustainable period by up to 22%, leading to a reduced number of required cores. The framework chooses the best parameters in 83% of cases and never selects parameters that cause deadline misses. Finally, we show that the overheads introduced by the skeleton abstraction layer are negligible.},
	address = {Sydney, Australia},
	author = {Paul Metzger and Murray Cole and Christian Fensch and Marco Aldinucci and Enrico Bini},
	booktitle = {26th {IEEE} Real-Time and Embedded Technology and Applications Symposium ({RTAS})},
	date-added = {2020-02-26 23:38:11 +0100},
	date-modified = {2020-02-27 00:13:59 +0100},
	doi = {10.1109/RTAS48715.2020.000-7},
	title = {Enforcing Deadlines for Skeleton-based Parallel Programming},
	url = {https://iris.unito.it/retrieve/handle/2318/1741320/616056/20_ske_RTAS.pdf},
	year = {2020},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1741320/616056/20_ske_RTAS.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/RTAS48715.2020.000-7}}

@inproceedings{20:sac:blockchain,
	abstract = {Our main use case features multiple companies that iteratively optimize on the architectural properties of aircraft components in a decentralized manner. In each optimization step of the so-called multi-disciplinary optimization (MDO) process, sensitive data is exchanged, and we require auditability and traceability of actions taken to assure compliance with signed legal agreements. In this paper, we present a distributed protocol that coordinates authenticated and auditable exchanges of files, leveraging a smart contract. The entire life cycle of a file exchange, including file registration, access request and key distribution, is recorded and traceable via the smart contract. Moreover, when one party raises a dispute, the smart contract can identify the dishonest party without compromising the file's confidentiality. The proposed protocol provides a simple, novel, yet efficient approach to exchange files with support for data access auditability between companies involved in a private consortium with no incentive to share files outside of the protocol. We implemented the protocol in Solidity, deployed it on a private Ethereum blockchain, and validated it within the use case of a decentralized workflow.},
	address = {New York, NY, USA},
	author = {Reniers, Vincent and Gao, Yuan and Zhang, Ren and Viviani, Paolo and Madhusudan, Akash and Lagaisse, Bert and Nikova, Svetla and Van Landuyt, Dimitri and Lombardi, Riccardo and Preneel, Bart and Joosen, Wouter},
	booktitle = {Proceedings of the 35th ACM/SIGAPP Symposium on Applied Computing},
	date-modified = {2021-04-24 23:21:47 +0200},
	isbn = {xxx-x-xxxx-xxxx-x},
	location = {Brno, Czech Republic},
	numpages = {8},
	pages = {1--8},
	publisher = {ACM},
	series = {SAC '20},
	title = {Authenticated and Auditable Data Sharing via Smart Contract},
	year = {2020}}

@inproceedings{20:looppar:pdp,
	abstract = {This work aims at distilling a systematic methodology to modernize existing sequential scientific codes with a limited re-designing effort, turning an old codebase into modern code, i.e., parallel and robust code. We propose an automatable methodology to parallelize scientific applications designed with a purely sequential programming mindset, thus possibly using global variables, aliasing, random number generators, and stateful functions. We demonstrate the methodology by way of an astrophysical application, where we model at the same time the kinematic profiles of 30 disk galaxies with a Monte Carlo Markov Chain (MCMC), which is sequential by definition. The parallel code exhibits a 12 times speedup on a 48-core platform.},
	address = {V{\"a}ster{\aa}s, Sweden},
	author = {Valentina Cesare and Iacopo Colonnelli and Marco Aldinucci},
	booktitle = {Proc. of 28th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-modified = {2025-05-24 09:40:24 +0200},
	doi = {10.1109/PDP50117.2020.00064},
	keywords = {HPC, parallel, hpc4ai, c3s},
	pages = {376--384},
	publisher = {IEEE},
	title = {Practical Parallelization of Scientific Applications},
	url = {https://iris.unito.it/retrieve/handle/2318/1735377/601141/2020_looppar_PDP.pdf},
	year = {2020},
	bdsk-url-1 = {https://doi.org/10.1109/PDP50117.2020.00064},
	bdsk-url-2 = {https://iris.unito.it/retrieve/handle/2318/1735377/601141/2020_looppar_PDP.pdf}}

@article{19:jsupe:grppi,
	abstract = {In the last years, pattern-based programming has been recognized as a good practice for efficiently exploiting parallel hardware resources. Following this approach, multiple libraries have been designed for providing such high-level abstractions to ease the parallel programming. However, those libraries do not share a common interface. To pave the way, GrPPI has been designed for providing an intermediate abstraction layer between application developers and existing parallel programming frameworks like OpenMP, Intel TBB or ISO C++ threads. On the other hand, FastFlow has been adopted as an efficient object-based programming framework that may benefit from being supported as an additional GrPPI backend. However, the object-based approach presents some major challenges to be incorporated under the GrPPI type safe functional programming style. In this paper, we present the integration of FastFlow as a new GrPPI backend to demonstrate that structured parallel programming frameworks perfectly fit the GrPPI design. Additionally, we also demonstrate that GrPPI does not incur in additional overheads for providing its abstraction layer, and we study the programmability in terms of lines of code and cyclomatic complexity. In general, the presented work acts as reciprocal validation of both FastFlow (as an efficient, native structured parallel programming framework) and GrPPI (as an efficient abstraction layer on top of existing parallel programming frameworks).},
	author = {Garcia, Jose Daniel and del Rio, Jose Daniel and Aldinucci, Marco and Tordini, Fabio and Danelutto, Marco and Mencagli, Gabriele and Torquati, Massimo},
	date-added = {2019-03-23 10:08:27 +0100},
	date-modified = {2020-11-15 19:15:18 +0100},
	doi = {10.1007/s11227-019-02826-5},
	journal = {The Journal of Supercomputing},
	keywords = {rephrase, fastflow},
	number = {7},
	pages = {5139--5159},
	title = {Challenging the abstraction penalty in parallel patterns libraries: Adding FastFlow support to GrPPI},
	url = {https://iris.unito.it/retrieve/handle/2318/1762686/744894/2020-js-grppi-postprint.pdf},
	volume = {76},
	year = {2020},
	bdsk-url-1 = {https://doi.org/10.1007/s11227-019-02826-5}}

@article{20:sms:chipset,
	abstract = {A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and projects to 152 relevant articles published in the period 2006--2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.},
	author = {Vasco Amaral and Beatriz Norberto and Miguel Goul{\~a}o and Marco Aldinucci and Siegfried Benkner and Andrea Bracciali and Paulo Carreira and Edgars Celms and Lu{\'\i}s Correia and Clemens Grelck and Helen Karatza and Christoph Kessler and Peter Kilpatrick and Hugo Martiniano and Ilias Mavridis and Sabri Pllana and Ana Resp{\'\i}cio and Jos{\'e} Sim{\~a}o and Lu{\'\i}s Veiga and Ari Visa},
	date-modified = {2020-11-15 17:21:48 +0100},
	doi = {https://doi.org/10.1016/j.parco.2019.102584},
	issn = {0167-8191},
	journal = {Parallel Computing},
	keywords = {HPC},
	pages = {102584},
	title = {Programming languages for data-Intensive HPC applications: A systematic mapping study},
	url = {https://iris.unito.it/retrieve/689605/1-s2.0-S0167819119301759-main.pdf},
	year = {2020},
	bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0167819119301759},
	bdsk-url-2 = {https://doi.org/10.1016/j.parco.2019.102584}}

@inproceedings{20:neohic:cibb,
	abstract = {High-throughput sequencing Chromosome Conformation Capture (Hi-C) allows the study of chromatin interactions and 3D chromosome folding on a larger scale. A graph-based multi-level representation of Hi-C data is essential for proper visualisation of the spatial pattern they represent, in particular for comparing different experiments or for re-mapping omics-data in a space-aware context. The size of the HiC data hampers the straightforward use of currently available graph visualisation tools and libraries. In this paper, we present the first version of NeoHiC, a user-friendly web application for the progressive graph visualisation of Hi-C data based on the use of the Neo4j graph database. The user could select the richness of the environment of the query gene by choosing among a large number of proximity and distance metrics.},
	author = {Daniele D'Agostino and Pietro Li{\`o} and Marco Aldinucci and Ivan Merelli},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	date-modified = {2025-05-24 09:21:10 +0200},
	doi = {10.1007/978-3-030-63061-4_10},
	isbn = {978-3-030-63061-4},
	keywords = {bioinformatics},
	pages = {98--107},
	title = {{NeoHiC}: A web application for the analysis of {Hi-C} data},
	url = {https://iris.unito.it/retrieve/handle/2318/1766001/690791/20_neohic_cibb.pdf},
	volume = {12313},
	year = {2020},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1766001/690791/20_neohic_cibb.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-030-63061-4_10}}

@inproceedings{DBLP:conf/esop/DagninoBZD20,
  author       = {Francesco Dagnino and
                  Viviana Bono and
                  Elena Zucca and
                  Mariangiola Dezani{-}Ciancaglini},
  editor       = {Peter M{\"{u}}ller},
  title        = {Soundness Conditions for Big-Step Semantics},
  booktitle    = {Programming Languages and Systems - 29th European Symposium on Programming,
                  {ESOP} 2020, Held as Part of the European Joint Conferences on Theory
                  and Practice of Software, {ETAPS} 2020, Dublin, Ireland, April 25-30,
                  2020, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {12075},
  pages        = {169--196},
  publisher    = {Springer},
  year         = {2020},
  url          = {https://doi.org/10.1007/978-3-030-44914-8\_7},
  doi          = {10.1007/978-3-030-44914-8\_7},
  timestamp    = {Fri, 14 May 2021 08:34:21 +0200},
  biburl       = {https://dblp.org/rec/conf/esop/DagninoBZD20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/lics/BonoD20,
  author       = {Viviana Bono and
                  Mariangiola Dezani{-}Ciancaglini},
  editor       = {Holger Hermanns and
                  Lijun Zhang and
                  Naoki Kobayashi and
                  Dale Miller},
  title        = {A tale of intersection types},
  booktitle    = {{LICS} '20: 35th Annual {ACM/IEEE} Symposium on Logic in Computer
                  Science, Saarbr{\"{u}}cken, Germany, July 8-11, 2020},
  pages        = {7--20},
  publisher    = {{ACM}},
  year         = {2020},
  url          = {https://doi.org/10.1145/3373718.3394733},
  doi          = {10.1145/3373718.3394733},
  timestamp    = {Sat, 30 Sep 2023 09:52:07 +0200},
  biburl       = {https://dblp.org/rec/conf/lics/BonoD20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2003-04996,
  author       = {Fabio Massimo Zanzotto and
                  Viviana Bono and
                  Paola Vocca and
                  Andrea Santilli and
                  Danilo Croce and
                  Giorgio Gambosi and
                  Roberto Basili},
  title        = {GASP! Generating Abstracts of Scientific Papers from Abstracts of
                  Cited Papers},
  journal      = {CoRR},
  volume       = {abs/2003.04996},
  year         = {2020},
  url          = {https://arxiv.org/abs/2003.04996},
  eprinttype   = {arXiv},
  eprint       = {2003.04996},
  timestamp    = {Tue, 17 Mar 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2003-04996.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tcs/MatosPR20,
  author       = {Armando B. Matos and
                  Luca Paolini and
                  Luca Roversi},
  title        = {The fixed point problem of a simple reversible language},
  journal      = {Theor. Comput. Sci.},
  volume       = {813},
  pages        = {143--154},
  year         = {2020},
  url          = {https://doi.org/10.1016/j.tcs.2019.10.005},
  doi          = {10.1016/J.TCS.2019.10.005},
  timestamp    = {Sun, 19 Jan 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/tcs/MatosPR20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tcs/PaoliniPR20,
  author       = {Luca Paolini and
                  Mauro Piccolo and
                  Luca Roversi},
  title        = {A class of Recursive Permutations which is Primitive Recursive complete},
  journal      = {Theor. Comput. Sci.},
  volume       = {813},
  pages        = {218--233},
  year         = {2020},
  url          = {https://doi.org/10.1016/j.tcs.2019.11.029},
  doi          = {10.1016/J.TCS.2019.11.029},
  timestamp    = {Sun, 19 Jan 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/tcs/PaoliniPR20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tcs/CurziR20,
  author       = {Gianluca Curzi and
                  Luca Roversi},
  title        = {A type-assignment of linear erasure and duplication},
  journal      = {Theor. Comput. Sci.},
  volume       = {837},
  pages        = {26--53},
  year         = {2020},
  url          = {https://doi.org/10.1016/j.tcs.2020.05.001},
  doi          = {10.1016/J.TCS.2020.05.001},
  timestamp    = {Tue, 25 Aug 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/tcs/CurziR20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/rc/MatosPR20,
  author       = {Armando B. Matos and
                  Luca Paolini and
                  Luca Roversi},
  editor       = {Ivan Lanese and
                  Mariusz Rawski},
  title        = {On the Expressivity of Total Reversible Programming Languages},
  booktitle    = {Reversible Computation - 12th International Conference, {RC} 2020,
                  Oslo, Norway, July 9-10, 2020, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {12227},
  pages        = {128--143},
  publisher    = {Springer},
  year         = {2020},
  url          = {https://doi.org/10.1007/978-3-030-52482-1\_7},
  doi          = {10.1007/978-3-030-52482-1\_7},
  timestamp    = {Thu, 06 Aug 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/rc/MatosPR20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-2007-01733,
  author       = {Gianluca Curzi and
                  Luca Roversi},
  title        = {Probabilistic Soft Type Assignment},
  journal      = {CoRR},
  volume       = {abs/2007.01733},
  year         = {2020},
  url          = {https://arxiv.org/abs/2007.01733},
  eprinttype   = {arXiv},
  eprint       = {2007.01733},
  timestamp    = {Mon, 06 Jul 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2007-01733.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/im/SpagnuoloCSPSKA20,
  author       = {Carmine Spagnuolo and
                  Gennaro Cordasco and
                  Przemyslaw Szufel and
                  Pawel Pralat and
                  Vittorio Scarano and
                  Bogumil Kaminski and
                  Alessia Antelmi},
  title        = {Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs
                  using SimpleHypergraphs.jl},
  journal      = {Internet Math.},
  volume       = {2020},
  year         = {2020},
  url          = {https://doi.org/10.24166/im.01.2020},
  doi          = {10.24166/IM.01.2020},
  timestamp    = {Mon, 03 Mar 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/im/SpagnuoloCSPSKA20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/atal/AntelmiCSS20,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Carmine Spagnuolo and
                  Vittorio Scarano},
  editor       = {Amal El Fallah Seghrouchni and
                  Gita Sukthankar and
                  Bo An and
                  Neil Yorke{-}Smith},
  title        = {A Design-Methodology for Epidemic Dynamics via Time-Varying Hypergraphs},
  booktitle    = {Proceedings of the 19th International Conference on Autonomous Agents
                  and Multiagent Systems, {AAMAS} '20, Auckland, New Zealand, May 9-13,
                  2020},
  pages        = {61--69},
  publisher    = {International Foundation for Autonomous Agents and Multiagent Systems},
  year         = {2020},
  url          = {https://dl.acm.org/doi/10.5555/3398761.3398774},
  doi          = {10.5555/3398761.3398774},
  timestamp    = {Tue, 26 Jul 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/atal/AntelmiCSS20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/waw/AntelmiCSS20,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Carmine Spagnuolo and
                  Przemyslaw Szufel},
  editor       = {Bogumil Kaminski and
                  Pawel Pralat and
                  Przemyslaw Szufel},
  title        = {Information Diffusion in Complex Networks: {A} Model Based on Hypergraphs
                  and Its Analysis},
  booktitle    = {Algorithms and Models for the Web Graph - 17th International Workshop,
                  {WAW} 2020, Warsaw, Poland, September 21-22, 2020, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {12091},
  pages        = {36--51},
  publisher    = {Springer},
  year         = {2020},
  url          = {https://doi.org/10.1007/978-3-030-48478-1\_3},
  doi          = {10.1007/978-3-030-48478-1\_3},
  timestamp    = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/waw/AntelmiCSS20.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{GARCIA:CCPE:19,
	abstract = {This paper presents PAMPAR, a new benchmark to evaluate the performance and energy consumption of different Parallel Programming Interfaces (PPIs). The benchmark is composed of 11 algorithms implemented in PThreads, OpenMP, MPI-1, and MPI-2 (spawn) PPIs. Previous studies have used some of these pseudo-applications to perform this type of evaluation in different architectures since there is no benchmark that offers this variety of PPIs and communication models. In this work, we measure the energy and performance of each pseudo-application in a single architecture, varying the number of threads/processes. We also organize the pseudo-applications according to their memory accesses, floating-point operations, and branches. The goal is to show that this set of pseudo-applications has enough features to build a parallel benchmark. The results show that there is no single best case that provides both better performance and low energy consumption in the presented scenarios. Moreover, the pseudo-applications usage of the system resources are different enough to represent different scenarios and be efficient as a benchmark.},
	author = {Garcia, Adriano Marques and Schepke, Claudio and Girardi, Alessandro Gon{\c{c}}alves},
	date-modified = {2024-08-18 14:42:47 +0200},
	doi = {10.1002/cpe.5504},
	journal = {Concurrency and Computation: Practice and Experience},
	keywords = {parallel},
	month = oct,
	number = {20},
	pages = {1--21},
	title = {PAMPAR: A new parallel benchmark for performance and energy consumption evaluation},
	url = {https://iris.unito.it/retrieve/d514c682-a567-4a02-93b7-9e27b6d3da03/Concurrency___Computation__Practice___Experience__Final_Version_.pdf},
	volume = {32},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/d514c682-a567-4a02-93b7-9e27b6d3da03/Concurrency___Computation__Practice___Experience__Final_Version_.pdf}}

@inproceedings{GARCIA:VECPAR:19,
	abstract = {This paper presents a new benchmark to evaluate performance and energy consumption of different Parallel Programming Interfaces (PPIs). The benchmark is composed of 11 algorithms implemented in PThreads, OpenMP, MPI-1 and MPI-2 (spawn) PPIs. Previous studies have used some of these applications to perform this type of evaluation in different architectures, since there is no benchmark that offers this variety of PPIs and communication models. In this work we measure the energy and performance of each application in a single architecture, varying the number of threads/processes. The goal is to show that this set of applications has enough features to form a parallel benchmark. The results show that there is no single best case that provides both better performance and low energy consumption in the presented scenarios. However, PThreads and OpenMP achieve the best trade-offs between performance and energy in most cases.},
	address = {Cham},
	author = {Garcia, Adriano Marques and Schepke, Claudio and Girardi, Alessandro Gon{\c{c}}alves and da Silva, Sherlon Almeida},
	booktitle = {High Performance Computing for Computational Science -- VECPAR 2018},
	date-modified = {2024-08-18 14:42:47 +0200},
	doi = {10.1007/978-3-030-15996-2_14},
	isbn = {978-3-030-15996-2},
	keywords = {parallel},
	month = mar,
	pages = {188--201},
	publisher = {Springer International Publishing},
	title = {A New Parallel Benchmark for Performance Evaluation and Energy Consumption},
	url = {https://iris.unito.it/retrieve/1272dea3-b1ea-4356-af0d-d180cef341b9/VECPAR_2018_paper_preprint.pdf},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/1272dea3-b1ea-4356-af0d-d180cef341b9/VECPAR_2018_paper_preprint.pdf}}

@article{19:eatcs:Medic19,
	abstract = {A number of formalisms have been proposed to model various approaches to reversibility and to better understand its properties and characteristics. However, the relation between these formalisms has hardly been studied. This paper examines the expressiveness of the causal-consistent reversibility in process algebras CCS and \emph{{\(\pi\)}}-calculus. In particular, we show, by means of encodings, that LTSs of two reversible extensions of CCS, Reversible CCS [1] and CCS with Keys [2], are isomorphic up to some structural transformations of processes. To study different causal semantics for ?-calculus, we devise a uniform framework for reversible \emph{{\(\pi\)}}-calculi that is parametric with respect to a data structure that stores information about the extrusion of a name. Depending on the used data structure, different causal semantics can be obtained. We show that reversibility induced by our framework when instantiated with three different data structures is causally-consistent and prove a causal correspondence between certain causal semantics and matching instance of the framework.},
	author = {Doriana Medi\'{c}},
	journal = {Bull. {EATCS}},
	keywords = {, semantics},
	title = {Relative expressiveness of calculi for reversible concurrency},
	url = {http://bulletin.eatcs.org/index.php/beatcs/article/view/590/601},
	volume = {129},
	year = {2019},
	bdsk-url-1 = {http://bulletin.eatcs.org/index.php/beatcs/article/view/590/601}}

@inproceedings{19:deeplearn:pdp,
	abstract = {This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.},
	address = {Pavia, Italy},
	author = {Paolo Viviani and Maurizio Drocco and Daniele Baccega and Iacopo Colonnelli and Marco Aldinucci},
	booktitle = {Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-added = {2020-01-30 10:48:12 +0100},
	date-modified = {2020-11-15 15:00:34 +0100},
	doi = {10.1109/EMPDP.2019.8671552},
	keywords = {ai},
	pages = {124--131},
	publisher = {IEEE},
	title = {Deep Learning at Scale},
	url = {https://iris.unito.it/retrieve/handle/2318/1695211/487778/19_deeplearning_PDP.pdf},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1695211/487778/19_deeplearning_PDP.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/EMPDP.2019.8671552}}

@article{19:merelli:jpdc,
	abstract = {Many bioinformatic applications require to exploit the capabilities of several computational resources to effectively access and process large and distributed datasets. In this context, Grid computing has been largely used to face unprecedented challenges in Computational Biology, at the cost of complex workarounds needed to make applications successfully running. The Grid computing paradigm, in fact, has always suffered from a lack of flexibility. Although this has been partially solved by Cloud computing, the on-demand approach is way distant from the original idea of volunteering computing that boosted the Grid paradigm. A solution to outpace the impossibility of creating custom environments for running applications in Grid is represented by the containerization technology. In this paper, we describe our experience in exploiting a Docker-based approach to run in a Grid environment a novel, computationally intensive, bioinformatic application, which models the DNA spatial conformation inside the nucleus of eukaryotic cells. Results assess the feasibility of this approach in terms of performance and efforts to run large experiments.},
	author = {Ivan Merelli and Federico Fornari and Fabio Tordini and Daniele D'Agostino and Marco Aldinucci and Daniele Cesini},
	date-added = {2019-09-04 15:24:03 +0200},
	date-modified = {2019-09-04 15:25:57 +0200},
	doi = {10.1016/j.jpdc.2019.08.002},
	issn = {0743-7315},
	journal = {Journal of Parallel and Distributed Computing},
	keywords = {bioinformatics},
	pages = {116--127},
	title = {Exploiting Docker containers over Grid computing for a comprehensive study of chromatin conformation in different cell types},
	url = {https://iris.unito.it/retrieve/handle/2318/1711684/532767/2019_Nuchart_JPDC_open.pdf},
	volume = {134},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1711684/532767/2019_Nuchart_JPDC_open.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.jpdc.2019.08.002}}

@misc{19:italia,
	abstract = {Il progetto DeepHealth, recentemente finanziato dalla Commissione Europea, ha come obiettivo la realizzazione di un ecosistema europeo costituito da piattaforme di calcolo ad alte prestazioni, librerie software e competenze multi-disciplinari di intelligenza artificiale, calcolo parallelo e scienze mediche per l'elaborazione e la diagnosi basata su immagini. Il contributo presenta sinteticamente le competenze e le infrastrutture nazionali coinvolte nel progetto.},
	author = {Marco Aldinucci and Claudio Berzovini and Costantino Grana and Marco Grangetto and Luca Pireddu and Gianluigi Zanetti},
	date-added = {2019-04-06 00:17:32 +0200},
	date-modified = {2021-03-27 00:03:43 +0100},
	howpublished = {{Ital-IA}: Convegno Nazionale CINI sull'Intelligenza Artificiale},
	keywords = {hpc4ai},
	month = mar,
	title = {Deep Learning e calcolo ad alte prestazioni per l'elaborazione di immagini biomediche},
	year = {2019}}

@inbook{Grelck2019,
	abstract = {Modelling and Simulation (M{\&}S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M{\&}S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications.},
	address = {Cham},
	author = {Grelck, Clemens and Niewiadomska-Szynkiewicz, Ewa and Aldinucci, Marco and Bracciali, Andrea and Larsson, Elisabeth},
	booktitle = {High-Performance Modelling and Simulation for Big Data Applications: Selected Results of the COST Action IC1406 cHiPSet},
	date-added = {2019-03-25 23:09:24 +0100},
	date-modified = {2019-03-25 23:09:42 +0100},
	doi = {10.1007/978-3-030-16272-6_1},
	editor = {Ko{\l}odziej, Joanna and Gonz{\'a}lez-V{\'e}lez, Horacio},
	isbn = {978-3-030-16272-6},
	keywords = {HPC},
	number = {11400},
	pages = {1--35},
	publisher = {Springer International Publishing},
	series = {LNCS},
	title = {Why High-Performance Modelling and Simulation for Big Data Applications Matters},
	url = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-16272-6_1.pdf},
	year = {2019},
	bdsk-url-1 = {https://link.springer.com/content/pdf/10.1007%2F978-3-030-16272-6_1.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-030-16272-6_1}}

@inproceedings{19:gsp:pdp,
	abstract = {The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.},
	address = {Pavia, Italy},
	author = {Maurizio Drocco and Paolo Viviani and Iacopo Colonnelli and Marco Aldinucci and Marco Grangetto},
	booktitle = {Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1109/EMPDP.2019.8671640},
	keywords = {grid},
	pages = {9--16},
	publisher = {IEEE},
	title = {Accelerating spectral graph analysis through wavefronts of linear algebra operations},
	url = {https://iris.unito.it/retrieve/handle/2318/1695315/488105/19_wavefront_PDP.pdf},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1695315/488105/19_wavefront_PDP.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/EMPDP.2019.8671640}}

@inproceedings{19:sac:blockchain,
	abstract = {Many applications by design depend on costly trusted third-party auditors. One such example is the industrial application case of federated multi-disciplinary optimization (MDO), in which different organizations contribute to a complex engineering design effort. Although blockchain and distributed ledger technology (DLT) has strong potential in reducing the dependence on such intermediaries, the architectural complexity involved in designing a solution is daunting. In this paper, we analyze the architectural variants for decentralized private data sharing while guaranteeing auditability in terms of data access operations. Non-repudiation of actions taken by each party is a key requirement, as is availability of the shared data. % through storage governed by the chain. The architectural variants analyzed focus on attaining:~(i)~confidential data exchange, (ii)~maintaining and governing access to the shared data, (iii)~providing data access auditability, (iv)~data validation or conflict resolution, and to a lesser degree (v)~transaction and identity privacy. We systematically enumerate architectural decisions at the levels of:~storage, policy-based file access control, data encryption methods, and auditability mechanisms for private data. This analysis is based on extensive assessment of the state of the art on decentralized private data access management using static or dynamic policies, and private data validation without exposing confidential information. The main contribution of this work is a comprehensive overview of architectural variants for decentralized control of private, encrypted data, and the involved trade-offs in terms of performance, auditable trust and security. These findings are validated in the context on the aforementioned industry case that involves federated multi-disciplinary optimization (MDO).},
	acmid = {3297316},
	address = {New York, NY, USA},
	author = {Reniers, Vincent and Van Landuyt, Dimitri and Viviani, Paolo and Lagaisse, Bert and Lombardi, Riccardo and Joosen, Wouter},
	booktitle = {Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing},
	date-modified = {2025-05-24 09:23:34 +0200},
	doi = {10.1145/3297280.3297316},
	isbn = {978-1-4503-5933-7},
	keywords = {system-sw},
	location = {Limassol, Cyprus},
	numpages = {9},
	pages = {346--354},
	publisher = {ACM},
	series = {SAC '19},
	title = {Analysis of Architectural Variants for Auditable Blockchain-based Private Data Sharing},
	url = {https://doi.acm.org/10.1145/3297280.3297316},
	year = {2019},
	bdsk-url-1 = {https://doi.acm.org/10.1145/3297280.3297316},
	bdsk-url-2 = {https://doi.org/10.1145/3297280.3297316}}

@article{18:dynqueue:ccpe,
	abstract = {Continuous streaming computations are usually composed of different modules, exchanging data through shared message queues. The selection of the algorithm used to access such queues (i.e. the concurrency control) is a critical aspect both for performance and power consumption. In this paper we describe the design of automatic concurrency control algorithm for implement- ing power-efficient communications on shared-memory multicores. The algorithm automatically switches between nonblocking and blocking concurrency protocols, getting the best from the two worlds, i.e. obtaining the same throughput offered by the nonblocking implementa- tion and the same power efficiency of the blocking concurrency protocol. We demonstrate the effectiveness of our approach using two micro-benchmarks and two real streaming applications},
	author = {Torquati, Massimo and De Sensi, Daniele and Mencagli, Gabriele and Aldinucci, Marco and Danelutto, Marco},
	date-added = {2018-03-13 16:50:46 +0000},
	date-modified = {2019-03-22 23:49:37 +0100},
	doi = {10.1002/cpe.4652},
	journal = {Concurrency and Computation: Practice and Experience},
	keywords = {rephrase},
	number = {5},
	title = {Power-Aware Pipelining with Automatic Concurrency Control},
	url = {https://iris.unito.it/retrieve/handle/2318/1668445/414282/2018_CCPE.pdf},
	volume = {31},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1668445/414282/2018_CCPE.pdf},
	bdsk-url-2 = {https://doi.org/10.1002/cpe.4652}}

@inbook{bigdata:encyclopedia:18,
	abstract = {In this chapter, some of the most common tools for Big Data analytics are surveyed, inter-alia, Apache Spark, Flink, Storm, and Beam. They are compared against well-defined features concerning programming model (language expressivity and semantics), and execution model (parallel behaviour and run-time support). The implementation of a running example is provided for all of them.},
	address = {Cham},
	author = {Aldinucci, Marco and Drocco, Maurizio and Misale, Claudia and Tremblay, Guy},
	booktitle = {Encyclopedia of Big Data Technologies},
	date-modified = {2024-04-01 11:30:19 +0200},
	doi = {10.1007/978-3-319-63962-8_142-1},
	editor = {Sakr, Sherif and Zomaya, Albert},
	isbn = {978-3-319-63962-8},
	keywords = {parallel},
	publisher = {Springer International Publishing},
	title = {Languages for Big Data analysis},
	url = {https://iris.unito.it/retrieve/handle/2318/1668051/413363/2019_bigdataframeworks_enc.pdf},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1668051/413363/2019_bigdataframeworks_enc.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-319-63962-8_142-1}}

@article{17:rephrasepatterns:ijpp,
	abstract = {We discuss the extended parallel pattern set identified within the EU-funded project RePhrase as a candidate pattern set to support data intensive applications targeting heterogeneous architectures. The set has been designed to include three classes of pattern, namely i) core patterns, modelling common, not necessarily data intensive parallelism exploitation patterns, usually to be used in composition; ii) high level patterns, modelling common, complex and complete parallelism exploitation patterns; and iii) building block patterns, modelling the single components of data intensive applications, suitable for use--in composition--to implement patterns not covered by the core and high level patterns. We discuss the expressive power of the RePhrase extended pattern set and results illustrating the performances that may be achieved with the FastFlow implementation of the high level patterns.},
	author = {Danelutto, Marco and De Matteis, Tiziano and De Sensi, Daniele and Mencagli, Gabriele and Torquati, Massimo and Aldinucci, Marco and Kilpatrick, Peter},
	date-added = {2017-11-25 15:30:59 +0000},
	date-modified = {2019-03-22 23:49:02 +0100},
	doi = {10.1007/s10766-017-0540-z},
	journal = {International Journal of Parallel Programming},
	keywords = {rephrase, fastflow},
	number = {1},
	pages = {74--93},
	title = {The RePhrase Extended Pattern Set for Data Intensive Parallel Computing},
	url = {https://iris.unito.it/retrieve/handle/2318/1659336/387667/2017_ijpp_rephrase.pdf},
	volume = {47},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1659336/387667/2017_ijpp_rephrase.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s10766-017-0540-z}}

@article{17:dmadasp:jsupe,
	abstract = {This work studies the issues related to dynamic memory management in Data Stream Processing, an emerging paradigm enabling the real-time processing of live data streams. In this paper we consider two streaming parallel patterns and we discuss  different implementation variants related on how dynamic memory is managed.  The results show that the standard mechanisms provided by modern C++ are not entirely adequate for maximizing the performance. Instead, the combined  use of an efficient general-purpose memory allocator, a custom allocator optimized for the pattern considered and a custom variant of the C++ shared pointer mechanism, provides a performance improvement up to 16{\%} on the best case.},
	author = {Torquati, Massimo and Mencagli, Gabriele and Drocco, Maurizio and Aldinucci, Marco and De Matteis, Tiziano and Danelutto, Marco},
	doi = {10.1007/s11227-017-2152-1},
	journal = {The Journal of Supercomputing},
	keywords = {rephrase, fastflow},
	number = {8},
	pages = {4114--4131},
	title = {On Dynamic Memory Allocation in Sliding-Window Parallel Patterns for Streaming Analytics},
	url = {https://iris.unito.it/retrieve/handle/2318/1648626/362381/17_torquati_jsc.pdf},
	volume = {75},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1648626/362381/17_torquati_jsc.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s11227-017-2152-1}}

@phdthesis{19:dl:viviani:thesis,
	abstract = {As deep learning techniques become more and more popular, there is the need to move these applications from the data scientist's Jupyter notebook to efficient and reliable enterprise solutions. Moreover, distributed training of deep learning models will happen more and more outside the well-known borders of cloud and HPC infrastructure and will move to edge and mobile platforms. Current techniques for distributed deep learning have drawbacks in both these scenarios, limiting their long-term applicability. After a critical review of the established techniques for Data Parallel training from both a distributed computing and deep learning perspective, a novel approach based on nearest-neighbour communications is presented in order to overcome some of the issues related to mainstream approaches, such as global communication patterns. Moreover, in order to validate the proposed strategy, the Flexible Asynchronous Scalable Training (FAST) framework is introduced, which allows to apply the nearest-neighbours communications approach to a deep learning framework of choice. Finally, a relevant use-case is deployed on a medium-scale infrastructure to demonstrate both the framework and the methodology presented. Training convergence and scalability results are presented and discussed in comparison to a baseline defined by using state-of-the-art distributed training tools provided by a well-known deep learning framework.},
	author = {Paolo Viviani},
	date-modified = {2024-03-26 14:26:55 +0100},
	doi = {10.5281/zenodo.3516093},
	month = sep,
	school = {Computer Science Department, University of Torino},
	title = {Deep Learning at Scale with Nearest Neighbours Communications},
	url = {https://zenodo.org/record/3516093/files/20190910_final_pdf.pdf},
	year = {2019},
	bdsk-url-1 = {https://zenodo.org/record/3516093/files/20190910_final_pdf.pdf},
	bdsk-url-2 = {https://doi.org/10.5281/zenodo.3516093}}

@inproceedings{2019EPJWC.21407030A,
	abstract = {Obtaining CPU cycles on an HPC cluster is nowadays relatively simple and sometimes even cheap for academic institutions. However, in most of the cases providers of HPC services would not allow changes on the configuration, implementation of special features or a lower-level control on the computing infrastructure, for example for testing experimental configurations. The variety of use cases proposed by several departments of the University of Torino, including ones from solid-state chemistry, computational biology, genomics and many others, called for different and sometimes conflicting configurations; furthermore, several R&D activities in the field of scientific computing, with topics ranging from GPU acceleration to Cloud Computing technologies, needed a platform to be carried out on. The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multi-purpose flexible HPC cluster designed and operated by a collaboration between the University of Torino and the Torino branch of the Istituto Nazionale di Fisica Nucleare. It is aimed at providing a flexible and reconfigurable infrastructure to cater to a wide range of different scientific computing needs, as well as a platform for R&D activities on computational technologies themselves. We describe some of the use cases that prompted the design and construction of the system, its architecture and a first characterisation of its performance by some synthetic benchmark tools and a few realistic use-case tests.},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2019EPJWC.21407030A},
	author = {Aldinucci, Marco and Bagnasco, Stefano and Concas, Matteo and Lusso, Stefano and Rabellino, Sergio and Demarchi, Danilo and Vallero, Sara},
	booktitle = {European Physical Journal Web of Conferences},
	doi = {10.1051/epjconf/201921407030},
	eid = {07030},
	month = jul,
	pages = {07030},
	series = {European Physical Journal Web of Conferences},
	title = {{Managing a heterogeneous scientific computing cluster with cloud-like tools: ideas and experience}},
	url = {https://iris.unito.it/retrieve/533279/epjconf_chep2018_07030.pdf},
	volume = {214},
	year = {2019},
	bdsk-url-1 = {https://iris.unito.it/retrieve/533279/epjconf_chep2018_07030.pdf},
	bdsk-url-2 = {https://doi.org/10.1051/epjconf/201921407030}}

@article{DBLP:journals/fuin/Bono19,
  author       = {Viviana Bono},
  title        = {The Magda Language: Ten Years After},
  journal      = {Fundam. Informaticae},
  volume       = {170},
  number       = {1-3},
  pages        = {15--38},
  year         = {2019},
  url          = {https://doi.org/10.3233/FI-2019-1854},
  doi          = {10.3233/FI-2019-1854},
  timestamp    = {Fri, 18 Sep 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/fuin/Bono19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1902-05369,
  author       = {Claudio Grandi and
                  Dariush Moshiri and
                  Luca Roversi},
  title        = {Introducing Yet Another REversible Language},
  journal      = {CoRR},
  volume       = {abs/1902.05369},
  year         = {2019},
  url          = {http://arxiv.org/abs/1902.05369},
  eprinttype   = {arXiv},
  eprint       = {1902.05369},
  timestamp    = {Tue, 21 May 2019 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1902-05369.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/asiasim/AntelmiCDVNS19,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Matteo D'Auria and
                  Daniele De Vinco and
                  Alberto Negro and
                  Carmine Spagnuolo},
  editor       = {Gary Tan and
                  Axel Lehmann and
                  Yong Meng Teo and
                  Wentong Cai},
  title        = {On Evaluating Rust as a Programming Language for the Future of Massive
                  Agent-Based Simulations},
  booktitle    = {Methods and Applications for Modeling and Simulation of Complex Systems
                  - 19th Asia Simulation Conference, AsiaSim 2019, Singapore, October
                  30 - November 1, 2019, Proceedings},
  series       = {Communications in Computer and Information Science},
  volume       = {1094},
  pages        = {15--28},
  publisher    = {Springer},
  year         = {2019},
  url          = {https://doi.org/10.1007/978-981-15-1078-6\_2},
  doi          = {10.1007/978-981-15-1078-6\_2},
  timestamp    = {Wed, 25 Feb 2026 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/asiasim/AntelmiCDVNS19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/um/Antelmi19,
  author       = {Alessia Antelmi},
  editor       = {George Angelos Papadopoulos and
                  George Samaras and
                  Stephan Weibelzahl and
                  Dietmar Jannach and
                  Olga C. Santos},
  title        = {Towards an Exhaustive Framework for Online Social Networks User Behaviour
                  Modelling},
  booktitle    = {Proceedings of the 27th {ACM} Conference on User Modeling, Adaptation
                  and Personalization, {UMAP} 2019, Larnaca, Cyprus, June 9-12, 2019},
  pages        = {349--352},
  publisher    = {{ACM}},
  year         = {2019},
  url          = {https://doi.org/10.1145/3320435.3323466},
  doi          = {10.1145/3320435.3323466},
  timestamp    = {Sun, 02 Nov 2025 21:27:29 +0100},
  biburl       = {https://dblp.org/rec/conf/um/Antelmi19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/waw/AntelmiCKPSSS19,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Bogumil Kaminski and
                  Pawel Pralat and
                  Vittorio Scarano and
                  Carmine Spagnuolo and
                  Przemyslaw Szufel},
  editor       = {Konstantin Avrachenkov and
                  Pawel Pralat and
                  Nan Ye},
  title        = {SimpleHypergraphs.jl - Novel Software Framework for Modelling and
                  Analysis of Hypergraphs},
  booktitle    = {Algorithms and Models for the Web Graph - 16th International Workshop,
                  {WAW} 2019, Brisbane, QLD, Australia, July 6-7, 2019, Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {11631},
  pages        = {115--129},
  publisher    = {Springer},
  year         = {2019},
  url          = {https://doi.org/10.1007/978-3-030-25070-6\_9},
  doi          = {10.1007/978-3-030-25070-6\_9},
  timestamp    = {Mon, 03 Mar 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/waw/AntelmiCKPSSS19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/www/AntelmiMS19,
  author       = {Alessia Antelmi and
                  Delfina Malandrino and
                  Vittorio Scarano},
  editor       = {Sihem Amer{-}Yahia and
                  Mohammad Mahdian and
                  Ashish Goel and
                  Geert{-}Jan Houben and
                  Kristina Lerman and
                  Julian J. McAuley and
                  Ricardo Baeza{-}Yates and
                  Leila Zia},
  title        = {Characterizing the Behavioral Evolution of Twitter Users and The Truth
                  Behind the 90-9-1 Rule},
  booktitle    = {Companion of The 2019 World Wide Web Conference, {WWW} 2019, San Francisco,
                  CA, USA, May 13-17, 2019},
  pages        = {1035--1038},
  publisher    = {{ACM}},
  year         = {2019},
  url          = {https://doi.org/10.1145/3308560.3316705},
  doi          = {10.1145/3308560.3316705},
  timestamp    = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/www/AntelmiMS19.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{GARCIA:WSCAD:18,
	abstract = {This paper presents a case study on the power consumption of different Parallel Programming Interfaces (PPIs) in multicore architectures. The study is based on the PAMPAR benchmark, which is composed of 11 algorithms implemented in PThreads, OpenMP, MPI-1 and MPI-2 (spawn) PPIs. The results show that there is no single best case that provides both better performance and low power consumption in the presented scenarios. However, PThreads and OpenMP achieve the best trade-offs between performance and power in most cases.},
	author = {Garcia, Adriano Marques and Schepke, Claudio and Girardi, Alessandro Gon{\c{c}}alves and da Silva, Sherlon Almeida},
	booktitle = {2018 Symposium on High Performance Computing Systems (WSCAD)},
	date-modified = {2024-08-18 14:42:47 +0200},
	doi = {10.1109/WSCAD.2018.00021},
	keywords = {parallel},
	month = oct,
	pages = {77--83},
	title = {Power Consumption of Parallel Programming Interfaces in Multicore Architectures: A Case Study},
	url = {https://iris.unito.it/retrieve/cab823a1-a6f7-483f-929a-607a166e0e78/A_Case_Study___Adriano___IEEE.pdf},
	year = {2018},
	bdsk-url-1 = {https://iris.unito.it/retrieve/cab823a1-a6f7-483f-929a-607a166e0e78/A_Case_Study___Adriano___IEEE.pdf}}

@article{18:arxiv:deeplearning,
	abstract = {This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvement. Beside, it presents a design for a practical C++ library dedicated at implementing and unifying the current state of the art methodologies for parallel training in a performance-conscious framework, allowing the user to explore novel strategies without departing significantly from its usual work-flow.},
	author = {Paolo Viviani and Maurizio Drocco and Marco Aldinucci},
	journal = {CoRR},
	title = {Pushing the boundaries of parallel Deep Learning - {A} practical approach},
	url = {https://arxiv.org/pdf/1806.09528},
	volume = {abs/1806.09528},
	year = {2018},
	bdsk-url-1 = {https://arxiv.org/pdf/1806.09528}}

@article{18:fgcs:pico,
	abstract = {In this paper, we present a new C++ API with a fluent interface called PiCo (Pipeline Composition). PiCo's programming model aims at making easier the programming of data analytics applications while preserving or enhancing their performance. This is attained through three key design choices: (1) unifying batch and stream data access models, (2) decoupling processing from data layout, and (3) exploiting a stream-oriented, scalable, efficient C++11 runtime system. PiCo proposes a programming model based on pipelines and operators that are polymorphic with respect to data types in the sense that it is possible to reuse the same algorithms and pipelines on different data models (e.g., streams, lists, sets, etc.). Preliminary results show that PiCo, when compared to Spark and Flink, can attain better performances in terms of execution times and can hugely improve memory utilization, both for batch and stream processing.},
	author = {Misale, Claudia and Drocco, Maurizio and Tremblay, Guy and Martinelli, Alberto R. and Aldinucci, Marco},
	booktitle = {Future Generation Computer Systems},
	date-added = {2018-05-18 21:24:31 +0000},
	date-modified = {2020-11-15 17:22:30 +0100},
	doi = {10.1016/j.future.2018.05.030},
	journal = {Future Generation Computer Systems},
	keywords = {toreador, HPC, fastflow},
	pages = {392--403},
	title = {PiCo: High-performance data analytics pipelines in modern C++},
	url = {https://iris.unito.it/retrieve/handle/2318/1668444/414280/fgcs_pico.pdf},
	volume = {87},
	year = {2018},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1668444/414280/fgcs_pico.pdf},
	bdsk-url-2 = {https://doi.org/10.1016/j.future.2018.05.030}}

@inproceedings{18:hpc4ai_acm_CF,
	abstract = {In April 2018, under the auspices of the POR-FESR 2014-2020 program of Italian Piedmont Region, the Turin's Centre on High-Performance Computing for Artificial Intelligence (HPC4AI) was funded with a capital investment of 4.5Me and it began its deployment. HPC4AI aims to facilitate scientific research and engineering in the areas of Artificial Intelligence and Big Data Analytics. HPC4AI will specifically focus on methods for the on-demand provisioning of AI and BDA Cloud services to the regional and national industrial community, which includes the large regional ecosystem of Small-Medium Enterprises (SMEs) active in many different sectors such as automotive, aerospace, mechatronics, manufacturing, health and agrifood.},
	address = {Ischia, Italy},
	author = {Marco Aldinucci and Sergio Rabellino and Marco Pironti and Filippo Spiga and Paolo Viviani and Maurizio Drocco and Marco Guerzoni and Guido Boella and Marco Mellia and Paolo Margara and Idillio Drago and Roberto Marturano and Guido Marchetto and Elio Piccolo and Stefano Bagnasco and Stefano Lusso and Sara Vallero and Giuseppe Attardi and Alex Barchiesi and Alberto Colla and Fulvio Galeazzi},
	booktitle = {ACM Computing Frontiers},
	date-added = {2018-04-21 14:18:48 +0000},
	date-modified = {2025-05-24 09:12:13 +0200},
	doi = {10.1145/3203217.3205340},
	keywords = {system-sw, hpc4ai, toreador, rephrase},
	month = may,
	title = {{HPC4AI}, an {AI-on-demand} federated platform endeavour},
	url = {https://iris.unito.it/retrieve/handle/2318/1765596/689772/2018_hpc4ai_ACM_CF.pdf},
	year = {2018},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1765596/689772/2018_hpc4ai_ACM_CF.pdf},
	bdsk-url-2 = {https://doi.org/10.1145/3203217.3205340}}

@inproceedings{svd:pdp:18,
	abstract = {The present trend in big-data analytics is to exploit algorithms with (sub-)linear time complexity, in this sense it is usually worth to  investigate if the available techniques can be approximated to reach an  affordable complexity. However, there are still problems in data science and  engineering that involve algorithms with higher time complexity, like matrix  inversion or Singular Value Decomposition (SVD). This work presents the results of a survey that reviews a number of tools meant  to perform dense linear algebra at ``Big Data'' scale: namely, the proposed  approach aims first to define a feasibility boundary for the problem size of  shared-memory matrix factorizations, then to understand whether it is  convenient to employ specific tools meant to scale out such dense linear algebra  tasks on distributed platforms. The survey will eventually discuss the presented tools from the point of view of  domain experts (data scientist, engineers), hence focusing on the trade-off  between usability and performance.},
	address = {Cambridge, United Kingdom},
	author = {Paolo Viviani and Maurizio Drocco and Marco Aldinucci},
	booktitle = {Proc. of 26th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-modified = {2025-05-24 09:20:01 +0200},
	doi = {10.1109/PDP2018.2018.00122},
	keywords = {parallel},
	publisher = {IEEE},
	title = {Scaling Dense Linear Algebra on Multicore and Beyond: a Survey},
	url = {https://iris.unito.it/retrieve/handle/2318/1659340/387685/preprint_aperto.pdf},
	year = {2018},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1659340/387685/preprint_aperto.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/PDP2018.2018.00122}}

@inproceedings{18:parco:workflow,
	abstract = {The cloud environment is increasingly appealing for the HPC community, which has always dealt with scientific applications. However, there is still some skepticism about moving from traditional physical infrastructures to virtual HPC clusters. This mistrusting probably originates from some well known factors, including the effective economy of using cloud services, data and software availability, and the longstanding matter of data stewardship. In this work we discuss the design of a framework (based on Mesos) aimed at achieving a cost-effective and efficient usage of heterogeneous Processing Elements (PEs) for workflow execution, which supports hybrid cloud bursting over preemptible cloud Virtual Machines.},
	author = {Fabio Tordini and Marco Aldinucci and Paolo Viviani and Ivan Merelli and Pietro Li{\`{o}}},
	booktitle = {Proc. of the Intl. Conference on Parallel Computing, ParCo 2017, 12-15 September 2017, Bologna, Italy},
	date-added = {2018-01-21 15:15:01 +0000},
	date-modified = {2018-03-13 16:44:11 +0000},
	doi = {10.3233/978-1-61499-843-3-605},
	keywords = {rephrase},
	publisher = {{IOS} Press},
	series = {Advances in Parallel Computing},
	title = {Scientific Workflows on Clouds with Heterogeneous and Preemptible Instances},
	url = {https://iris.unito.it/retrieve/handle/2318/1658510/385411/main.pdf},
	year = {2018},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1658510/385411/main.pdf},
	bdsk-url-2 = {https://doi.org/10.3233/978-1-61499-843-3-605}}

@inproceedings{pico:autodasp:17,
	abstract = {In this paper, we present a new C++ API with a fluent interface called PiCo (Pipeline Composition). PiCo's programming model aims at making easier the programming of data analytics applications while preserving or enhancing their performance. This is attained through three key design choices: 1) unifying batch and stream data access models, 2) decoupling processing from data layout, and 3) exploiting a stream-oriented, scalable, effiicient C++11 runtime system. PiCo proposes a programming model based on pipelines and operators that are polymorphic with respect to data types in the sense that it is possible to re-use the same algorithms and pipelines on different data models (e.g., streams, lists, sets, etc.). Preliminary results show that PiCo can attain better performances in terms of execution times and hugely improve memory utilization when compared to Spark and Flink in both batch and stream processing.},
	address = {Santiago de Compostela, Spain},
	author = {Claudia Misale and Maurizio Drocco and Guy Tremblay and Marco Aldinucci},
	booktitle = {Proc. of Euro-Par Workshops: 1st Intl. Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017)},
	date-added = {2017-11-12 10:17:46 +0000},
	date-modified = {2024-03-26 14:26:42 +0100},
	doi = {10.1007/978-3-319-75178-8_10},
	keywords = {rephrase, toreador},
	month = aug,
	publisher = {Springer},
	series = {{LNCS}},
	title = {PiCo: a Novel Approach to Stream Data Analytics},
	url = {https://iris.unito.it/retrieve/handle/2318/1659344/409520/autodasp.pdf},
	volume = {10659},
	year = {2018},
	bdsk-url-1 = {https://dx.doi.org/10.1007/978-3-319-75178-8_10},
	bdsk-url-2 = {https://iris.unito.it/retrieve/handle/2318/1659344/409520/autodasp.pdf}}

@inbook{17:viviani:advstruct,
	abstract = {This work presents an innovative approach adopted for the development of a new numerical software framework for accelerating dense linear algebra calculations and its application within an engineering context. In particular, response surface models (RSM) are a key tool to reduce the computational effort involved in engineering design processes like design optimization. However, RSMs may prove to be too expensive to be computed when the dimensionality of the system and/or the size of the dataset to be synthesized is significantly high or when a large number of different response surfaces has to be calculated in order to improve the overall accuracy (e.g. like when using ensemble modelling techniques). On the other hand, the potential of modern hybrid hardware (e.g. multicore, GPUs) is not exploited by current engineering tools, while they can lead to a significant performance improvement. To fill this gap, a software framework is being developed that enables the hybrid and scalable acceleration of the linear algebra core for engineering applications and especially of RSMs calculations with a user-friendly syntax that allows good portability between different hardware architectures, with no need of specific expertise in parallel programming and accelerator technology. The effectiveness of this framework is shown by comparing an accelerated code to a single-core calculation of a radial basis function RSM on some benchmark datasets. This approach is then validated within a real-life engineering application and the achievements are presented and discussed.},
	address = {Cham},
	author = {Viviani, Paolo and Aldinucci, Marco and d'Ippolito, Roberto and Lemeire, Jan and Vucinic, Dean},
	booktitle = {Improved Performance of Materials: Design and Experimental Approaches},
	date-modified = {2018-03-13 16:40:21 +0000},
	doi = {10.1007/978-3-319-59590-0_9},
	isbn = {978-3-319-59590-0},
	keywords = {repara, rephrase},
	opteditor = {{\"O}chsner, Andreas and Altenbach, Holm},
	pages = {93--106},
	publisher = {Springer International Publishing},
	title = {A Flexible Numerical Framework for Engineering---A Response Surface Modelling Application},
	year = {2018},
	bdsk-url-1 = {https://doi.org/10.1007/978-3-319-59590-0_9}}

@article{16:stencilreduce:jsupe,
	abstract = {We advocate the Loop-of-stencil-reduce pattern as a means of simplifying the implementation of data-parallel programs on heterogeneous multi-core platforms. Loop-of-stencil-reduce is general enough to subsume map, reduce, map-reduce, stencil, stencil-reduce, and, crucially, their usage in a loop in both data-parallel and streaming applications, or a combination of both. The pattern makes it possible to deploy a single stencil computation kernel on different GPUs. We discuss the implementation of Loop-of-stencil-reduce in FastFlow, a framework for the implementation of applications based on the parallel patterns. Experiments are presented to illustrate the use of Loop-of-stencil-reduce in developing data-parallel kernels running on heterogeneous systems.},
	author = {Aldinucci, Marco and Danelutto, Marco and Drocco, Maurizio and Kilpatrick, Peter and Misale, Claudia and Peretti Pezzi, Guilherme and Torquati, Massimo},
	date-added = {2016-08-19 21:52:17 +0000},
	date-modified = {2018-12-27 18:19:41 +0100},
	doi = {10.1007/s11227-016-1871-z},
	journal = {Journal of Supercomputing},
	keywords = {HPC, repara, rephrase},
	number = {11},
	pages = {5690--5705},
	title = {A Parallel Pattern for Iterative Stencil + Reduce},
	url = {https://iris.unito.it/retrieve/0716fc42-53d7-48c0-9469-697aabfe7759/jspaper.pdf},
	volume = {74},
	year = {2018},
	bdsk-url-1 = {http://dx.doi.org/10.1007/s11227-016-1871-z},
	bdsk-url-2 = {http://arxiv.org/pdf/1609.04567v1.pdf}}

@article{17:slidingwindows:jpdc,
	abstract = {Abstract According to the recent trend in data acquisition and processing technology, big data are increasingly available in the form of unbounded streams of elementary data items to be processed in real-time. In this paper we study in detail the paradigm of sliding windows, a well-known technique for approximated queries that update their results continuously as new fresh data arrive from the stream. In this work we focus on the relationship between the various existing sliding window semantics and the way the query processing is performed from the parallelism perspective. From this study two alternative parallel models are identified, each covering semantics with very precise properties. Each model is described in terms of its pros and cons, and parallel implementations in the FastFlow framework are analyzed by discussing the layout of the concurrent data structures used for the efficient windows representation in each model.},
	author = {Gabriele Mencagli and Massimo Torquati and Fabio Lucattini and Salvatore Cuomo and Marco Aldinucci},
	date-modified = {2018-12-27 18:24:34 +0100},
	doi = {10.1016/j.jpdc.2017.10.021},
	issn = {0743-7315},
	journal = {Journal of Parallel and Distributed Computing},
	keywords = {rephrase, fastflow},
	month = jun,
	pages = {74--88},
	title = {Harnessing sliding-window execution semantics for parallel stream processing},
	url = {https://iris.unito.it/retrieve/e27ce42c-1381-2581-e053-d805fe0acbaa/preprint-jpdc-2017.pdf},
	volume = {116},
	year = {2018},
	bdsk-url-1 = {https://iris.unito.it/retrieve/e27ce42c-1381-2581-e053-d805fe0acbaa/preprint-jpdc-2017.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1016/j.jpdc.2017.10.021},
	bdsk-url-3 = {https://iris.unito.it/retrieve/e27ce42c-1381-2581-e053-d805fe0acbaa/preprint-jpdc-2017.pdf}}

@article{DBLP:journals/lmcs/BettiniBDGV18,
  author       = {Lorenzo Bettini and
                  Viviana Bono and
                  Mariangiola Dezani{-}Ciancaglini and
                  Paola Giannini and
                  Betti Venneri},
  title        = {Java {\&} Lambda: a Featherweight Story},
  journal      = {Log. Methods Comput. Sci.},
  volume       = {14},
  number       = {3},
  year         = {2018},
  url          = {https://doi.org/10.23638/LMCS-14(3:17)2018},
  doi          = {10.23638/LMCS-14(3:17)2018},
  timestamp    = {Thu, 14 Oct 2021 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/lmcs/BettiniBDGV18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/ngc/PaoliniPR18,
  author       = {Luca Paolini and
                  Mauro Piccolo and
                  Luca Roversi},
  title        = {On a Class of Reversible Primitive Recursive Functions and Its Turing-Complete
                  Extensions},
  journal      = {New Gener. Comput.},
  volume       = {36},
  number       = {3},
  pages        = {233--256},
  year         = {2018},
  url          = {https://doi.org/10.1007/s00354-018-0039-1},
  doi          = {10.1007/S00354-018-0039-1},
  timestamp    = {Thu, 14 May 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/ngc/PaoliniPR18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ictcs/MatosPR18,
  author       = {Armando B. Matos and
                  Luca Paolini and
                  Luca Roversi},
  editor       = {Alessandro Aldini and
                  Marco Bernardo},
  title        = {The Fixed Point Problem for General and for Linear {SRL} Programs
                  is Undecidable},
  booktitle    = {Proceedings of the 19th Italian Conference on Theoretical Computer
                  Science, Urbino, Italy, September 18-20, 2018},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {2243},
  pages        = {128--139},
  publisher    = {CEUR-WS.org},
  year         = {2018},
  url          = {https://ceur-ws.org/Vol-2243/paper12.pdf},
  timestamp    = {Fri, 10 Mar 2023 16:23:17 +0100},
  biburl       = {https://dblp.org/rec/conf/ictcs/MatosPR18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:journals/corr/abs-1711-00774,
  author       = {Luca Paolini and
                  Luca Roversi and
                  Margherita Zorzi},
  editor       = {Thomas Ehrhard and
                  Maribel Fern{\'{a}}ndez and
                  Valeria de Paiva and
                  Lorenzo Tortora de Falco},
  title        = {Quantum programming made easy},
  booktitle    = {Proceedings Joint International Workshop on Linearity {\&} Trends
                  in Linear Logic and Applications, Linearity-TLLA@FLoC 2018, Oxford,
                  UK, 7-8 July 2018},
  series       = {{EPTCS}},
  volume       = {292},
  pages        = {133--147},
  year         = {2018},
  url          = {https://doi.org/10.4204/EPTCS.292.8},
  doi          = {10.4204/EPTCS.292.8},
  timestamp    = {Sun, 19 Jan 2025 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1711-00774.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1804-08105,
  author       = {Luca Roversi},
  title        = {Subatomic systems need not be subatomic},
  journal      = {CoRR},
  volume       = {abs/1804.08105},
  year         = {2018},
  url          = {http://arxiv.org/abs/1804.08105},
  eprinttype   = {arXiv},
  eprint       = {1804.08105},
  timestamp    = {Mon, 13 Aug 2018 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1804-08105.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/aics/AntelmiGY18,
  author       = {Alessia Antelmi and
                  Josephine Griffith and
                  Karen Young},
  editor       = {Rob Brennan and
                  J{\"{o}}ran Beel and
                  Ruth Byrne and
                  Jeremy Debattista and
                  Ademar Crotti Junior},
  title        = {Towards a More Systematic Analysis of Twitter Data: {A} Framework
                  for the Analysis of Twitter Communities},
  booktitle    = {Proceedings for the 26th {AIAI} Irish Conference on Artificial Intelligence
                  and Cognitive Science Trinity College Dublin, Dublin, Ireland, December
                  6-7th, 2018},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {2259},
  pages        = {303--314},
  publisher    = {CEUR-WS.org},
  year         = {2018},
  url          = {https://ceur-ws.org/Vol-2259/aics\_28.pdf},
  timestamp    = {Fri, 10 Mar 2023 16:22:44 +0100},
  biburl       = {https://dblp.org/rec/conf/aics/AntelmiGY18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/bigdataconf/AntelmiMS18,
  author       = {Alessia Antelmi and
                  Delfina Malandrino and
                  Vittorio Scarano},
  editor       = {Naoki Abe and
                  Huan Liu and
                  Calton Pu and
                  Xiaohua Hu and
                  Nesreen K. Ahmed and
                  Mu Qiao and
                  Yang Song and
                  Donald Kossmann and
                  Bing Liu and
                  Kisung Lee and
                  Jiliang Tang and
                  Jingrui He and
                  Jeffrey S. Saltz},
  title        = {Characterizing Twitter Users: : What do Samantha Cristoforetti, Barack
                  Obama and Britney Spears Have in Common?},
  booktitle    = {{IEEE} International Conference on Big Data {(IEEE} BigData 2018),
                  Seattle, WA, USA, December 10-13, 2018},
  pages        = {3622--3627},
  publisher    = {{IEEE}},
  year         = {2018},
  url          = {https://doi.org/10.1109/BigData.2018.8622045},
  doi          = {10.1109/BIGDATA.2018.8622045},
  timestamp    = {Fri, 19 Nov 2021 16:08:20 +0100},
  biburl       = {https://dblp.org/rec/conf/bigdataconf/AntelmiMS18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/gamesem/AntelnniBY18,
  author       = {Alessia Antelmi and
                  John G. Breslin and
                  Karen Young},
  title        = {Understanding User Engagement with Entertainment Media: {A} Case Study
                  of the Twitter Behaviour of Game of Thrones (GoT) Fans},
  booktitle    = {{IEEE} Games, Entertainment, Media Conference, {GEM} 2018, Galway,
                  Ireland, August 15-17, 2018},
  pages        = {1--9},
  publisher    = {{IEEE}},
  year         = {2018},
  url          = {https://doi.org/10.1109/GEM.2018.8516505},
  doi          = {10.1109/GEM.2018.8516505},
  timestamp    = {Tue, 28 Feb 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/gamesem/AntelnniBY18.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{17:ijpp:cuomo:editorial,
	abstract = {
		Performance is still the hottest keyword in parallel and distributed systems: performance evaluation, design for performance, performance portability and scalability are just a few of the many possible declinations that nowadays are of paramount scientific importance. To tackle these challenges, system architects, applications programmers and data center managers need methodological tools to fit at best the overall workload and the available architecture, maximizing the overall performances and minimizing overheads, energy consumption or idle time while application developers mainly aim at algorithmic and software oriented performances. Proper methodologies for modeling and analysis are the way to turn complexity into opportunities.

		This Special Issue of the International Journal of Parallel Programming welcomes papers that present practical and methodological approaches to analytical and simulative performance evaluation for architecturally complex systems and high-performance parallel and computing algorithm. Successful contributions have been done on specific technologies, applications and innovative solutions to system specifications and algorithmic schemes both.
	},
	annote = {editorial},
	author = {Cuomo, Salvatore and Aldinucci, Marco and Torquati, Massimo},
	date-added = {2017-11-12 11:10:10 +0000},
	date-modified = {2024-04-01 11:32:42 +0200},
	doi = {10.1007/s10766-017-0531-0},
	issn = {0885-7458},
	journal = {International Journal of Parallel Programming},
	keywords = {HPC},
	month = oct,
	note = {Editorial},
	pages = {1--3},
	title = {Guest Editorial for Programming Models and Algorithms for Data Analysis in HPC Systems},
	url = {https://doi.org/10.1007/s10766-017-0531-0},
	year = {2017},
	bdsk-url-1 = {https://doi.org/10.1007/s10766-017-0531-0},
	bdsk-url-2 = {https://dx.doi.org/10.1007/s10766-017-0531-0},
	bdsk-url-3 = {https://doi.org/10.1007/s10766-017-0531-0}}

@phdthesis{17:gam:drocco:thesis,
	abstract = {In the realm of High Performance Computing (HPC), message passing has been the programming paradigm of choice for over twenty years. The durable MPI (Message Passing Interface) standard, with send/receive communication, broadcast, gather/scatter, and reduction collectives is still used to construct parallel programs where each communication is orchestrated by the de\-vel\-oper-based precise knowledge of data distribution and overheads; collective communications simplify the orchestration but might induce excessive synchronization. Early attempts to bring shared-memory programming model---with its programming adv\-antages---to distributed computing, referred as the Distributed Shared Memory (DSM) model, faded away; one of the main issue was to combine performance and programmability with the memory consistency model. The recently proposed Partitioned Global Address Space (PGAS) model is a modern revamp of DSM that exposes data placement to enable optimizations based on locality, but it still addresses (simple) data-parallelism only and it relies on expensive sharing protocols. We advocate an alternative programming model for distributed computing based on a Global Asynchronous Memory (GAM), aiming to \emph{avoid} coherency and consistency problems rather than solving them. We materialize GAM by designing and implementing a \emph{distributed smart pointers} library, inspired by C++ smart pointers. In this model, public and private pointers (resembling C++ shared and unique pointers, respectively) are moved around instead of messages (i.e., data), thus alleviating the user from the burden of minimizing transfers. On top of smart pointers, we propose a high-level C++ template library for writing applications in terms of dataflow-like networks, namely GAM nets, consisting of stateful processors exchanging pointers in fully asynchronous fashion. We demonstrate the validity of the proposed approach, from the expressiveness perspective, by showing how GAM nets can be exploited to implement higher-level parallel programming models, such as data and task parallelism. As for the performance perspective, the execution of two non-toy benchmarks on a number of different small-scale HPC clusters exhibits both close-to-ideal scalability and negligible overhead with respect to state-of-the-art benchmark implementations. For instance, the GAM implementation of a high-quality video restoration filter sustains a 100 fps throughput over 70\%-noisy high-quality video streams on a 4-node cluster of Graphics Processing Units (GPUs), with minimal programming effort.},
	author = {Maurizio Drocco},
	date-modified = {2017-12-12 15:09:35 +0000},
	doi = {10.5281/zenodo.1037585},
	keywords = {fastflow, rephrase, toreador, repara, paraphrase},
	month = oct,
	school = {Computer Science Department, University of Torino},
	title = {Parallel Programming with Global Asynchronous Memory: Models, {C++} {API}s and Implementations},
	url = {https://zenodo.org/record/1037585/files/Drocco_phd_thesis.pdf},
	year = {2017},
	bdsk-url-1 = {https://zenodo.org/record/1037585/files/Drocco_phd_thesis.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.5281/zenodo.1037585}}

@inproceedings{17:hlpp:powerstream,
	abstract = {Power consumption management has become a major concern in software development. Continuous streaming computations are usually com- posed by different modules, exchanging data through shared message queues. The selection of the algorithm used to access such queues (i.e., the concurrency control) is a critical aspect for both performance and power consumption. In this paper, we describe the design of an adaptive concurrency control algo- rithm for implementing power-efficient communications on shared memory multicores. The algorithm provides the throughput offered by a nonblocking implementation and the power efficiency of a blocking protocol. We demon- strate that our algorithm reduces the power consumption of data streaming computations without decreasing their throughput.},
	address = {Valladolid, Spain},
	author = {Aldinucci, Marco and Danelutto, Marco and De Sensi, Daniele and Mencagli, Gabriele and Torquati, Massimo},
	booktitle = {Proceedings of the 10th International Symposium on High-Level Parallel Programming and Applications},
	date-added = {2017-07-13 09:02:32 +0000},
	date-modified = {2018-12-27 18:53:27 +0100},
	keywords = {rephrase, fastflow},
	title = {Towards Power-Aware Data Pipelining on Multicores},
	url = {https://iris.unito.it/retrieve/handle/2318/1644982/351415/17_HLPP_powerstream.pdf},
	year = {2017},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1644982/351415/17_HLPP_powerstream.pdf}}

@techreport{17:drocco:techreport,
	author = {Drocco, Maurizio and Misale, Claudia and Tremblay, Guy and Aldinucci, Marco},
	date-added = {2017-06-19 15:45:02 +0000},
	date-modified = {2018-03-13 16:45:57 +0000},
	doi = {10.5281/zenodo.571802},
	institution = {Computer Science Department, University of Torino},
	keywords = {rephrase, toreador},
	month = may,
	note = {https://arxiv.org/abs/1705.01629},
	title = {A Formal Semantics for Data Analytics Pipelines},
	url = {https://doi.org/10.5281/zenodo.571802},
	year = {2017},
	bdsk-url-1 = {https://doi.org/10.5281/zenodo.571802},
	bdsk-url-2 = {http://dx.doi.org/10.5281/zenodo.571802}}

@phdthesis{17:pico:misale:thesis,
	abstract = {
		In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models---for which only informal (and often confusing) semantics is generally provided---all share a common under- lying model, namely, the Dataflow model. Using this model as a starting point, it is possible to categorize and analyze almost all aspects about Big Data analytics tools from a high level perspective. This analysis can be considered as a first step toward a formal model to be exploited in the design of a (new) framework for Big Data analytics. By putting clear separations between all levels of abstraction (i.e., from the runtime to the user API), it is easier for a programmer or software designer to avoid mixing low level with high level aspects, as we are often used to see in state-of-the-art Big Data analytics frameworks.

		From the user-level perspective, we think that a clearer and simple semantics is preferable, together with a strong separation of concerns. For this reason, we use the Dataflow model as a starting point to build a programming environment with a simplified programming model implemented as a Domain-Specific Language, that is on top of a stack of layers that build a prototypical framework for Big Data analytics.

		The contribution of this thesis is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm, Google Dataflow), thus making it easier to understand high-level data-processing applications written in such frameworks. As result of this analysis, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.

		Second, we propose a programming environment based on such layered model in the form of a Domain-Specific Language (DSL) for processing data collections, called PiCo (Pipeline Composition). The main entity of this programming model is the Pipeline, basically a DAG-composition of processing elements. This model is intended to give the user an unique interface for both stream and batch processing, hiding completely data management and focusing only on operations, which are represented by Pipeline stages. Our DSL will be built on top of the FastFlow library, exploiting both shared and distributed parallelism, and implemented in C++11/14 with the aim of porting C++ into the Big Data world.
	},
	author = {Claudia Misale},
	date-added = {2017-06-19 15:15:52 +0000},
	date-modified = {2017-06-19 15:55:21 +0000},
	doi = {10.5281/zenodo.579753},
	keywords = {fastflow, rephrase, toreador, repara, paraphrase},
	month = may,
	school = {Computer Science Department, University of Torino},
	title = {PiCo: A Domain-Specific Language for Data Analytics Pipelines},
	url = {https://iris.unito.it/retrieve/handle/2318/1633743/320170/Misale_thesis.pdf},
	year = {2017},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1633743/320170/Misale_thesis.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.5281/zenodo.579753}}

@article{17:bigdatasurvey:PPL,
	abstract = {In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models, for which only informal (and often confusing) semantics is generally provided, all share a common underlying model, namely, the Dataflow model. The Dataflow model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to understand high-level data-processing applications written in such frameworks. Second, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.},
	author = {Misale, Claudia and Drocco, Maurizio and Aldinucci, Marco and Tremblay, Guy},
	date-modified = {2024-03-26 14:26:17 +0100},
	date-published = {March 2017},
	date-received = {January 2017},
	doi = {10.1142/S0129626417400035},
	eprint = {http://www.worldscientific.com/doi/pdf/10.1142/S0129626417400035},
	journal = {Parallel Processing Letters},
	keywords = {toreador, rephrase},
	number = {01},
	pages = {1--20},
	title = {A Comparison of Big Data Frameworks on a Layered Dataflow Model},
	url = {https://iris.unito.it/retrieve/handle/2318/1626287/303421/preprintPPL_4aperto.pdf},
	volume = {27},
	year = {2017},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1626287/303421/preprintPPL_4aperto.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1142/S0129626417400035}}

@inproceedings{17:sac:armadillo,
	abstract = {The Armadillo C++ library provides programmers with a high-level Matlab-like syntax for linear algebra. Its design aims at providing a good balance between speed and ease of use. It can be linked with different back-ends, i.e. different LAPACK-compliant libraries. In this work we present a novel run-time support of Armadillo, which gracefully extends mainstream implementation to enable back-end switching without recompilation and multiple back-end support. The extension is specifically designed to not affect Armadillo class template prototypes, thus to be easily interoperable with future evolutions of the Armadillo library itself. The proposed software stack is then tested for functionality and performance against a kernel code extracted from an industrial application.},
	address = {Marrakesh, Morocco},
	author = {Paolo Viviani and Massimo Torquati and Marco Aldinucci and Roberto d'Ippolito},
	booktitle = {In proc. of the 32nd ACM Symposium on Applied Computing (SAC)},
	date-added = {2016-08-19 21:47:45 +0000},
	date-modified = {2017-06-13 15:54:43 +0000},
	keywords = {HPC, repara, rephrase},
	month = apr,
	pages = {1566--1573},
	title = {Multiple back-end support for the Armadillo linear algebra interface},
	url = {https://iris.unito.it/retrieve/handle/2318/1626229/299089/armadillo_4aperto.pdf},
	year = {2017},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1626229/299089/armadillo_4aperto.pdf}}

@inbook{ff:wileybook:17,
	abstract = {This chapter first outlines FastFlow design and then shows sample use of the FastFlow programming environment together with performance results achieved on various state-of-the-art multicore architectures. The FastFlow framework has been designed according to four foundational principles: layered design; efficiency in base mechanisms; support for stream parallelism; and a programming model based on design pattern/algorithmic skeleton concepts. The core of the FastFlow framework provides an efficient implementation of single-producer-single-consumer (SPSC) first in-first out (FIFO) queues. The next tier up extends from one-to-one queues to one-to-many, many-to-one, and many-to-many synchronizations and data flows, which are implemented using only SPSC queues and arbiter threads, thus providing lock-free and wait-free arbitrary dataflow graphs. When designing and implementing new parallel applications using FastFlow, programmers instantiate patterns provided by FastFlow to adapt them to the specific needs of the application at hand. The chapter demonstrates how the principal FastFlow patterns may be used in a parallel application.},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Massimo Torquati},
	booktitle = {Programming Multi-core and Many-core Computing Systems},
	chapter = {13},
	date-added = {2011-06-18 18:28:00 +0200},
	date-modified = {2014-12-31 14:14:28 +0000},
	doi = {10.1002/9781119332015.ch13},
	editor = {Sabri Pllana and Fatos Xhafa},
	isbn = {9781119332015},
	keywords = {fastflow},
	optannote = {ISBN: 0470936908},
	pages = {261--280},
	publisher = {John Wiley \& Sons, Ltd},
	series = {Parallel and Distributed Computing},
	title = {FastFlow: high-level and efficient streaming on multi-core},
	url = {http://calvados.di.unipi.it/storage/paper_files/2011_FF_tutorial-draft.pdf},
	year = {2017},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2011_FF_tutorial-draft.pdf},
	bdsk-url-2 = {https://dx.doi.org/10.1002/9781119332015.ch13}}

@article{16:ijhpca:nuchart,
	abstract = {Recent advances in molecular biology and bioinformatics techniques brought to an explosion of the information about the spatial organisation of the DNA in the nucleus of a cell. High-throughput molecular biology techniques provide a genome-wide capture of the spatial organization of chromosomes at unprecedented scales, which permit to identify physical interactions between genetic elements located throughout a genome. Recent results have shown that there is a large correlation between co-localization and co-regulation of genes, but these important information are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we present NuChart-II, an efficient and highly optimized tool for genomic data analysis that provides a gene-centric, graph-based representation of genomic information. While designing NuChart-II we addressed several common issues in the parallelisation of memory bound algorithms for shared-memory systems. With performance and usability in mind, NuChart-II is a R package that embeds a C++ engine: computing capabilities and memory hierarchy of multi-core architectures are fully exploited, while the versatile R environment for statistical analysis and data visualisation rises the level of abstraction and permits to orchestrate analysis and visualisation of genomic data.},
	author = {Fabio Tordini and Maurizio Drocco and Claudia Misale and Luciano Milanesi and Pietro Li{\`o} and Ivan Merelli and Massimo Torquati and Marco Aldinucci},
	date-modified = {2018-12-27 19:06:22 +0100},
	doi = {10.1177/1094342016668567},
	journal = {International Journal of High Performance Computing Applications},
	keywords = {fastflow, bioinformatics, repara, rephrase, bioinformatics},
	number = {3},
	pages = {196--211},
	title = {{NuChart-II}: the road to a fast and scalable tool for {Hi-C} data analysis},
	url = {https://iris.unito.it/retrieve/handle/2318/1607126/238747/main.pdf},
	volume = {31},
	year = {2017},
	bdsk-url-1 = {http://hdl.handle.net/2318/1607126},
	bdsk-url-2 = {http://dx.doi.org/10.1177/1094342016668567},
	bdsk-url-3 = {https://iris.unito.it/retrieve/handle/2318/1607126/238747/main.pdf}}

@inproceedings{16:occam:chep,
	abstract = {Obtaining CPU cycles on an HPC cluster is nowadays relatively simple and sometimes even cheap for academic institutions. However, in most of the cases providers of HPC services would not allow changes on the configuration, implementation of special features or a lower-level control on the computing infrastructure and networks, for example for testing new computing patterns or conducting research on HPC itself. The variety of use cases proposed by several departments of the University of Torino, including ones from solid-state chemistry, high-energy physics, computer science, big data analytics, computational biology, genomics and many others, called for different and sometimes conflicting configurations; furthermore, several R&D activities in the field of scientific computing, with topics ranging from GPU acceleration to Cloud Computing technologies, needed a platform to be carried out on. The Open Computing Cluster for Advanced data Manipulation (OCCAM) is a multi-purpose flexible HPC cluster designed and operated by a collaboration between the University of Torino and the Torino branch of the Istituto Nazionale di Fisica Nucleare. It is aimed at providing a flexible, reconfigurable and extendable infrastructure to cater to a wide range of different scientific computing needs, as well as a platform for R&D activities on computational technologies themselves. Extending it with novel architecture CPU, accelerator or hybrid microarchitecture (such as forthcoming Intel Xeon Phi Knights Landing) should be as a simple as plugging a node in a rack. The initial system counts slightly more than 1100 cpu cores and includes different types of computing nodes (standard dual-socket nodes, large quad-sockets nodes with 768 GB RAM, and multi-GPU nodes) and two separate disk storage subsystems: a smaller high-performance scratch area, based on the Lustre file system, intended for direct computational I/O and a larger one, of the order of 1PB, to archive near-line data for archival purposes. All the components of the system are interconnected through a 10Gb/s Ethernet layer with one-level topology and an InfiniBand FDR 56Gbps layer in fat-tree topology. A system of this kind, heterogeneous and reconfigurable by design, poses a number of challenges related to the frequency at which heterogeneous hardware resources might change their availability and shareability status, which in turn affect methods and means to allocate, manage, optimize, bill, monitor VMs, virtual farms, jobs, interactive bare-metal sessions, etc. This poster describes some of the use cases that prompted the design ad construction of the HPC cluster, its architecture and a first characterization of its performance by some synthetic benchmark tools and a few realistic use-case tests.},
	address = {San Francisco, USA},
	author = {Marco Aldinucci and Stefano Bagnasco and Stefano Lusso and Paolo Pasteris and Sergio Rabellino},
	booktitle = {Journal of Physics: Conf. Series (CHEP 2016)},
	date-modified = {2025-05-24 09:13:23 +0200},
	doi = {10.1088/1742-6596/898/8/082039},
	keywords = {system-sw, HPC, c3s},
	number = {8},
	optmonth = {oct},
	pages = {082039},
	title = {OCCAM: a flexible, multi-purpose and extendable HPC cluster},
	url = {http://iopscience.iop.org/article/10.1088/1742-6596/898/8/082039/meta},
	volume = {898},
	year = {2017},
	bdsk-url-1 = {http://iopscience.iop.org/article/10.1088/1742-6596/898/8/082039/meta},
	bdsk-url-2 = {http://dx.doi.org/10.1088/1742-6596/898/8/082039}}

@article{17:deepx:conce,
	abstract = {Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich and Pyle (G&P) method or the Tanner--Whitehouse (TW) one. However, both clinical procedures show several limitations, from the examination effort of radiologists to (most importantly) significant intra- and inter-operator variability. To address these problems, several automated approaches (especially relying on the TW method) have been proposed; nevertheless, none of them has been proved able to generalize to different races, age ranges and genders. In this paper, we propose and test several deep learning approaches to assess skeletal bone age automatically; the results showed an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state-of-the-art performance. Furthermore, this is the first automated skeletal bone age assessment work tested on a public dataset and for all age ranges, races and genders, for which the source code is available, thus representing an exhaustive baseline for future research in the field. Beside the specific application scenario, this paper aims at providing answers to more general questions about deep learning on medical images: from the comparison between deep-learned features and manually-crafted ones, to the usage of deep-learning methods trained on general imagery for medical problems, to how to train a CNN with few images.},
	author = {Concetto Spampinato and Simone Palazzo and Daniela Giordano and Marco Aldinucci and Rosalia Leonardi},
	doi = {10.1016/j.media.2016.10.010},
	journal = {Medical Image Analysis},
	keywords = {HPC},
	pages = {41--51},
	title = {Deep learning for automated skeletal bone age assessment in X-ray images},
	url = {https://iris.unito.it/retrieve/e27ce42b-5743-2581-e053-d805fe0acbaa/main.pdf},
	volume = {36},
	year = {2017},
	bdsk-url-1 = {http://dx.doi.org/10.1016/j.media.2016.10.010},
	bdsk-url-2 = {https://iris.unito.it/retrieve/e27ce42b-5743-2581-e053-d805fe0acbaa/main.pdf}}

@article{lmcs:3725,
	author = {Severi, Paula and Padovani, Luca and Tuosto, Emilio and Dezani{-}Ciancaglini, Mariangiola},
	doi = {10.23638/LMCS-13(2:9)2017},
	journal = {{Logical Methods in Computer Science}},
	keywords = {rephrase, semantics},
	month = jun,
	title = {On Sessions and Infinite Data},
	url = {http://lmcs.episciences.org/3725},
	volume = {{Volume 13, Issue 2}},
	year = {2017},
	bdsk-url-1 = {http://lmcs.episciences.org/3725},
	bdsk-url-2 = {http://dx.doi.org/10.23638/LMCS-13(2:9)2017}}

@inproceedings{CDMZ16,
	author = {Mario Coppo and Mariangiola Dezani{-}Ciancaglini and Alejandro D\'{\i}az-Caro and Ines Margaria and Maddalena Zacchi},
	booktitle = {ITRS'16},
	doi = {10.4204/EPTCS.242.5},
	editor = {Naoki Kobayashi},
	keywords = {rephrase, semantics},
	pages = {31--47},
	series = {EPTCS},
	title = {Retractions in Intersection Types},
	url = {http://www.di.unito.it/~dezani/papers/cddmz.pdf},
	volume = {242},
	year = {2017},
	bdsk-url-1 = {http://www.di.unito.it/~dezani/papers/cddmz.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.4204/EPTCS.242.5}}

@inproceedings{DBLP:conf/sc/RomeroPRFSG17,
	author = {Romero, Joshua and Phillips, Everett H. and Ruetsch, Gregory and Fatica, Massimiliano and Spiga, Filippo and Giannozzi, Paolo},
	bibsource = {dblp computer science bibliography, http://dblp.org},
	biburl = {http://dblp.org/rec/bib/conf/sc/RomeroPRFSG17},
	booktitle = {High Performance Computing Systems. Performance Modeling, Benchmarking, and Simulation - 8th International Workshop, {PMBS} 2017, Denver, CO, USA, November 13, 2017, Proceedings},
	doi = {10.1007/978-3-319-72971-8_4},
	optcrossref = {DBLP:conf/sc/2017pmbs},
	pages = {67--87},
	timestamp = {Wed, 03 Jan 2018 18:14:11 +0100},
	title = {A Performance Study of Quantum ESPRESSO's PWscf Code on Multi-core and {GPU} Systems},
	url = {https://doi.org/10.1007/978-3-319-72971-8_4},
	year = {2017},
	bdsk-url-1 = {https://doi.org/10.1007/978-3-319-72971-8_4}}

@inproceedings{DBLP:conf/ipps/AhmadBBBBCFGGLS17,
	author = {Ahmad, Wissam Abu and Bartolini, Andrea and Beneventi, Francesco and Benini, Luca and Borghesi, Andrea and Cicala, Marco and Forestieri, Privato and Gianfreda, Cosimo and Gregori, Daniele and Libri, Antonio and Spiga, Filippo and Tinti, Simone},
	bibsource = {dblp computer science bibliography, http://dblp.org},
	biburl = {http://dblp.org/rec/bib/conf/ipps/AhmadBBBBCFGGLS17},
	booktitle = {2017 {IEEE} International Parallel and Distributed Processing Symposium Workshops, {IPDPS} Workshops 2017, Orlando / Buena Vista, FL, USA, May 29 - June 2, 2017},
	date-modified = {2024-04-01 11:28:31 +0200},
	doi = {10.1109/IPDPSW.2017.22},
	keywords = {hpc},
	optcrossref = {DBLP:conf/ipps/2017w},
	pages = {964--973},
	timestamp = {Mon, 10 Jul 2017 21:06:07 +0200},
	title = {Design of an Energy Aware Petaflops Class High Performance Cluster Based on Power Architecture},
	url = {https://doi.org/10.1109/IPDPSW.2017.22},
	year = {2017},
	bdsk-url-1 = {https://doi.org/10.1109/IPDPSW.2017.22}}

@article{DBLP:journals/jlp/BonoCDV17,
  author       = {Viviana Bono and
                  Mario Coppo and
                  Mariangiola Dezani{-}Ciancaglini and
                  Betti Venneri},
  title        = {Data-driven adaptation for smart sessions},
  journal      = {J. Log. Algebraic Methods Program.},
  volume       = {90},
  pages        = {31--49},
  year         = {2017},
  url          = {https://doi.org/10.1016/j.jlamp.2017.02.007},
  doi          = {10.1016/J.JLAMP.2017.02.007},
  timestamp    = {Sat, 22 Feb 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/jlp/BonoCDV17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{16:ccgrid:misale,
	abstract = {Big data analytics is an indispensable tool in transforming science, engineering, medicine, healthcare, finance and ultimately business itself. With the explosion of data sizes and need for shorter time-to-solution, in-memory platforms such as Apache Spark gain increasing popularity. However, this introduces important challenges, among which data shuffling is particularly difficult: on one hand it is a key part of the computation that has a major impact on the overall performance and scalability so its efficiency is paramount, while on the other hand it needs to operate with scarce memory in order to leave as much memory available for data caching. In this context, efficient scheduling of data transfers such that it addresses both dimensions of the problem simultaneously is non-trivial. State-of-the-art solutions often rely on simple approaches that yield sub optimal performance and resource usage. This paper contributes a novel shuffle data transfer strategy that dynamically adapts to the computation with minimal memory utilization, which we briefly underline as a series of design principles.},
	address = {Cartagena, Colombia},
	author = {Nicolae, Bogdan and Costa, Carlos H. A. and Misale, Claudia and Katrinis, Kostas and Park, Yoonho},
	booktitle = {{IEEE/ACM} 16th Intl. Symposium on Cluster, Cloud and Grid Computing, CCGrid 2016},
	date-modified = {2024-04-01 11:18:22 +0200},
	doi = {10.1109/CCGrid.2016.85},
	keywords = {HPC},
	publisher = {IEEE},
	title = {Towards Memory-Optimized Data Shuffling Patterns for Big Data Analytics},
	url = {http://ieeexplore.ieee.org/document/7515716/},
	year = {2016},
	bdsk-url-1 = {http://ieeexplore.ieee.org/document/7515716/},
	bdsk-url-2 = {http://dx.doi.org/10.1109/CCGrid.2016.85}}

@inproceedings{16:acaces:armadillo,
	abstract = {The aim of this work is to provide developers and domain experts with simple (Matlab-like) inter- face for performing linear algebra tasks while retaining state-of-the-art computational speed. To achieve this goal we extend Armadillo C++ library is extended in order to support with multiple LAPACK-compliant back-ends targeting different architectures including CUDA GPUs; moreover our approach involves the possibility of dynamically switching between such back-ends in order to select the one which is most convenient based on the specific problem and hardware configura- tion. This approach is eventually validated within an industrial environment.},
	address = {Fiuggi, Italy},
	author = {Paolo Viviani and Marco Aldinucci and Roberto d'Ippolito},
	booktitle = {Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) -- Poster Abstracts},
	date-added = {2016-08-20 17:22:51 +0000},
	date-modified = {2020-11-15 15:01:16 +0100},
	keywords = {HPC, repara},
	month = jul,
	title = {An hybrid linear algebra framework for engineering},
	url = {https://iris.unito.it/retrieve/handle/2318/1622382/300198/armadillo.pdf},
	year = {2016},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1622382/300198/armadillo.pdf}}

@inproceedings{skedato:pdp:16,
	abstract = {In this paper we present a novel approach for functional-style programming of distributed-memory clusters, targeting data-centric applications. The programming model proposed is purely sequential, SPMD-free and based on high- level functional features introduced since C++11 specification. Additionally, we propose a novel cluster-as-accelerator design principle. In this scheme, cluster nodes act as general inter- preters of user-defined functional tasks over node-local portions of distributed data structures. We envision coupling a simple yet powerful programming model with a lightweight, locality- aware distributed runtime as a promising step along the road towards high-performance data analytics, in particular under the perspective of the upcoming exascale era. We implemented the proposed approach in SkeDaTo, a prototyping C++ library of data-parallel skeletons exploiting cluster-as-accelerator at the bottom layer of the runtime software stack.},
	address = {Crete, Greece},
	author = {Maurizio Drocco and Claudia Misale and Marco Aldinucci},
	booktitle = {Proc. of 24th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-modified = {2017-12-12 14:49:31 +0000},
	doi = {10.1109/PDP.2016.97},
	keywords = {rephrase, fastflow},
	pages = {350--353},
	publisher = {IEEE},
	title = {A Cluster-As-Accelerator approach for {SPMD}-free Data Parallelism},
	url = {https://iris.unito.it/retrieve/handle/2318/1611858/262689/2016_pdp_skedato.pdf},
	year = {2016},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1611858/262689/2016_pdp_skedato.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/PDP.2016.97}}

@inproceedings{rpl:pdp:16,
	abstract = {Parallelising sequential applications is usually a very hard job, due to many different ways in which an application can be parallelised and a large number of programming models (each with its own advantages and disadvantages) that can be used. In this paper, we describe a method to semi- automatically generate and evaluate different parallelisations of the same application, allowing programmers to find the best parallelisation without significant manual reengineering of the code. We describe a novel, high-level domain-specific language, Refactoring Pattern Language (RPL), that is used to represent the parallel structure of an application and to capture its extra-functional properties (such as service time). We then describe a set of RPL rewrite rules that can be used to generate alternative, but semantically equivalent, parallel structures (parallelisations) of the same application. We also describe the RPL Shell that can be used to evaluate these parallelisations, in terms of the desired extra-functional properties. Finally, we describe a set of C++ refactorings, targeting OpenMP, Intel TBB and FastFlow parallel programming models, that semi-automatically apply the desired parallelisation to the application's source code, therefore giving a parallel version of the code. We demonstrate how the RPL and the refactoring rules can be used to derive efficient parallelisations of two realistic C++ use cases (Image Convolution and Ant Colony Optimisation).},
	address = {Crete, Greece},
	author = {Janjic, Vladimir and Brown, Christopher and MacKenzie, Kenneth and Hammond, Kevin and Danelutto, Marco and Aldinucci, Marco and Garc{\'{\i}}a, Jos{\'{e}} Daniel},
	booktitle = {Proc. of Intl. Euromicro PDP 2016: Parallel Distributed and network-based Processing},
	date-modified = {2017-06-20 08:19:39 +0000},
	doi = {10.1109/PDP.2016.122},
	keywords = {rephrase, fastflow},
	publisher = {IEEE},
	title = {{RPL}: A Domain-Specific Language for Designing and Implementing Parallel C++ Applications},
	url = {https://iris.unito.it/retrieve/handle/2318/1597172/299237/2016_jsupe_stencil_pp_4aperto.pdf},
	year = {2016},
	bdsk-url-1 = {http://hdl.handle.net/2318/1597172},
	bdsk-url-2 = {https://iris.unito.it/retrieve/handle/2318/1597172/299237/2016_jsupe_stencil_pp_4aperto.pdf},
	bdsk-url-3 = {http://dx.doi.org/10.1109/PDP.2016.122}}

@article{pool:ijpp:15,
	abstract = {We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.},
	author = {Marco Aldinucci and Sonia Campa and Marco Danelutto and Peter Kilpatrick and Massimo Torquati},
	date-added = {2015-03-21 22:15:47 +0000},
	date-modified = {2015-09-24 11:15:53 +0000},
	doi = {10.1007/s10766-015-0358-5},
	issn = {0885-7458},
	journal = {International Journal of Parallel Programming},
	keywords = {fastflow, paraphrase, repara},
	number = {3},
	pages = {531--551},
	publisher = {Springer US},
	title = {Pool Evolution: A Parallel Pattern for Evolutionary and Symbolic Computing},
	url = {https://iris.unito.it/retrieve/handle/2318/1522392/42139/2015_ff_pool_ijpp.pdf},
	volume = {44},
	year = {2016},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1522392/42139/2015_ff_pool_ijpp.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s10766-015-0358-5}}

@incollection{15:lnbi:nuchaRt,
	abstract = {Recent advances in molecular biology and Bioinformatics techniques brought to an explosion of the information about the spatial organisation of the DNA in the nucleus. High-throughput chromosome conformation capture techniques provide a genome-wide capture of chromatin contacts at unprecedented scales, which permit to identify physical interactions between genetic elements located throughout the human genome. These important studies are hampered by the lack of biologists-friendly software. In this work we present NuchaRt, an R package that wraps NuChart-II, an efficient and highly optimized C++ tool for the exploration of Hi-C data. By rising the level of abstraction, NuchaRt proposes a high-performance pipeline that allows users to orchestrate analysis and visualisation of multi-omics data, making optimal use of the computing capabilities offered by modern multi-core architectures, combined with the versatile and well known R environment for statistical analysis and data visualisation.},
	address = {Cham (ZG)},
	author = {Fabio Tordini and Ivan Merelli and Pietro Li{\`o} and Luciano Milanesi and Marco Aldinucci},
	booktitle = {Computational Intelligence Methods for Bioinformatics and Biostatistics},
	doi = {10.1007/978-3-319-44332-4},
	editor = {Springer International Publishing},
	isbn = {978-3-319-44331-7},
	keywords = {fastflow, bioinformatics, repara, bioinformatics},
	pages = {259--272},
	publisher = {Springer International Publishing},
	series = {{Lecture Notes in Computer Science}},
	title = {{NuchaRt}: embedding high-level parallel computing in {R} for augmented {Hi-C} data analysis},
	url = {https://iris.unito.it/retrieve/handle/2318/1608281/253372/rnuchart.pdf},
	volume = {9874},
	year = {2016},
	bdsk-url-1 = {http://link.springer.com/book/10.1007%2F978-3-319-44332-4},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-319-44332-4}}

@inproceedings{16:scalcom:cloud,
	abstract = {Recent advances in molecular biology and Bioinformatics techniques have brought to an explosion of the information about the spatial organisation of the DNA inside the nucleus. In particular, 3C-based techniques are revealing the genome folding for many different cell types, and permit to create a more effective representation of the disposition of genes in the three-dimensional space. This information can be used to re-interpret heterogeneous genomic data (multi-omic) relying on 3D maps of the chromosome. The storage and computational requirements needed to accomplish such operations on raw sequenced data have to be fulfilled using HPC solutions, and the the Cloud paradigm is a valuable and convenient mean for delivering HPC to Bioinformatics. In this work we describe a data analysis work-flow that allows the integration and the interpretation of multi-omic data on a sort of ``topographical'' nuclear map, capable of representing the effective disposition of genes in a graph-based representation. We propose a cloud-based task farm pattern to orchestrate the services needed to accomplish genomic data analysis, where each service represents a special-purpose tool, playing a part in well known data analysis pipelines.},
	author = {Fabio Tordini},
	booktitle = {Proceedings of the 16th IEEE International Conference on Scalable Computing and Communication},
	date-modified = {2016-08-30 10:26:12 +0000},
	doi = {10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.131},
	keywords = {fastflow, bioinformatics, rephrase},
	note = {Best paper award},
	pages = {559--566},
	publisher = {IEEE Computer Society},
	title = {{A cloud solution for multi-omics data integration}},
	url = {http://calvados.di.unipi.it/storage/paper_files/2016_cloudpipeline_scalcom.pdf},
	year = {2016},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2016_cloudpipeline_scalcom.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.131}}

@phdthesis{tordiniThesis16,
	abstract = {Nowadays, molecular biology laboratories are delivering more and more data about DNA organisation, at increasing resolution and in a large number of samples. So much that genomic research is now facing many of the scale-out issues that high-performance computing has been addressing for years: they require powerful infrastructures with fast computing and storage capabilities, with substantial challenges in terms of data processing, statistical analysis and data representation. With this thesis we propose a high-performance pipeline for the analysis and interpretation of heterogeneous genomic information: beside performance, usability and availability are two essential requirements that novel Bioinformatics tools should satisfy. In this perspective, we propose and discuss our efforts towards a solid infrastructure for data processing and storage, where software that operates over data is exposed as a service, and is accessible by users through the Internet. We begin by presenting NuChart-II, a tool for the analysis and interpretation of spatial genomic information. With NuChart-II we propose a graph-based representation of genomic data, which can provide insights on the disposition of genomic elements in the DNA. We also discuss our approach for the normalisation of biases that affect raw sequenced data. We believe that many currently available tools for genomic data analysis are perceived as tricky and troublesome applications, that require highly specialised skills to obtain the desired outcomes. Concerning usability, we want to rise the level of abstraction perceived by the user, but maintain high performance and correctness while providing an exhaustive solution for data visualisation. We also intend to foster the availability of novel tools: in this work we also discuss a cloud solution that delivers computation and storage as dynamically allocated virtual resources via the Internet, while needed software is provided as a service. In this way, the computational demand of genomic research can be satisfied more economically by using lab-scale and enterprise-oriented technologies. Here we discuss our idea of a task farm for the integration of heterogeneous data resulting from different sequencing experiments: we believe that the integration of multi-omic features on a nuclear map can be a valuable mean for studying the interactions among genetic elements. This can reveal insights on biological mechanisms, such as genes regulation, translocations and epigenetic patterns.},
	author = {Fabio Tordini},
	keywords = {fastflow, bioinformatics},
	month = apr,
	school = {Computer Science Department, University of Torino, Italy},
	title = {{The road towards a Cloud-based High-Performance solution for genomic data analysis}},
	url = {http://calvados.di.unipi.it/storage/paper_files/2016_tordini_phdthesis.pdf},
	year = {2016},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2016_tordini_phdthesis.pdf}}

@inproceedings{16:PMAM:SPSC,
	acmid = {2883406},
	address = {New York, NY, USA},
	author = {Dolz, Manuel F. and del Rio Astorga, David and Fern{\'{a}}ndez, Javier and Garc{\'{\i}}a, Jos{\'{e}} Daniel and Garc{\'{\i}}a Carballeira, F{\'{e}}lix and Danelutto, Marco and Torquati, Massimo},
	booktitle = {Proceedings of the 7th International Workshop on Programming Models and Applications for Multicores and Manycores},
	date-modified = {2016-04-21 17:33:36 +0000},
	doi = {10.1145/2883404.2883406},
	isbn = {978-1-4503-4196-7},
	keywords = {fastflow, repara},
	location = {Barcelona, Spain},
	numpages = {10},
	pages = {20--29},
	publisher = {ACM},
	series = {PMAM'16},
	title = {Embedding Semantics of the Single-Producer/Single-Consumer Lock-Free Queue into a Race Detection Tool},
	url = {https://doi.acm.org/10.1145/2883404.2883406},
	year = {2016},
	bdsk-url-1 = {https://doi.acm.org/10.1145/2883404.2883406},
	bdsk-url-2 = {http://dx.doi.org/10.1145/2883404.2883406}}

@article{16:pwhatshap:bmc,
	abstract = {Background: Haplotype phasing is an important problem in the analysis of genomics information. Given a set of DNA fragments of an individual, it consists of determining which one of the possible alleles (alternative forms of a gene) each fragment comes from. Haplotype information is relevant to gene regulation, epigenetics, genome-wide association studies, evolutionary and population studies, and the study of mutations. Haplotyping is currently addressed as an optimisation problem aiming at solutions that minimise, for instance, error correction costs, where costs are a measure of the confidence in the accuracy of the information acquired from DNA sequencing. Solutions have typically an exponential computational complexity. WhatsHap is a recent optimal approach which moves computational complexity from DNA fragment length to fragment overlap, i.e., coverage, and is hence of particular interest when considering sequencing technology's current trends that are producing longer fragments. Results: Given the potential relevance of efficient haplotyping in several analysis pipelines, we have designed and engineered pWhatsHap, a parallel, high-performance version of WhatsHap. pWhatsHap is embedded in a toolkit developed in Python and supports genomics datasets in standard file formats. Building on WhatsHap, pWhatsHap exhibits the same complexity exploring a number of possible solutions which is exponential in the coverage of the dataset. The parallel implementation on multi-core architectures allows for a relevant reduction of the execution time for haplotyping, while the provided results enjoy the same high accuracy as that provided by WhatsHap, which increases with coverage. Conclusions: Due to its structure and management of the large datasets, the parallelisation of WhatsHap posed demanding technical challenges, which have been addressed exploiting a high-level parallel programming framework. The result, pWhatsHap, is a freely available toolkit that improves the efficiency of the analysis of genomics information.},
	author = {Andrea Bracciali and Marco Aldinucci and Murray Patterson and Tobias Marschall and Nadia Pisanti and Ivan Merelli and Massimo Torquati},
	date-modified = {2016-10-17 17:28:27 +0000},
	doi = {10.1186/s12859-016-1170-y},
	journal = {BMC Bioinformatics},
	keywords = {fastflow, paraphrase, rephrase},
	number = {Suppl 11},
	pages = {342},
	title = {pWhatsHap: efficient haplotyping for future generation sequencing},
	url = {http://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1170-y?site=bmcbioinformatics.biomedcentral.com},
	volume = {17},
	year = {2016},
	bdsk-url-1 = {http://hdl.handle.net/2318/1607125},
	bdsk-url-2 = {http://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-016-1170-y?site=bmcbioinformatics.biomedcentral.com},
	bdsk-url-3 = {http://dx.doi.org/10.1186/s12859-016-1170-y}}

@article{2016_omics_fgenetics,
	abstract = {Publicly available multi-omic databases, in particular if associated with medical annotations, are rich resources with the potential to lead a rapid transition from high-throughput molecular biology experiments to better clinical outcomes for patients. In this work, we propose a model for multi-omic data integration (i.e. genetic variations, gene expression, genome conformation and epigenetic patterns), which exploits a multi-layer network approach to analyse, visualize and obtain insights from such biological information, in order to use achieved results at a macroscopic level. Using this representation, we can describe how driver and passenger mutations accumulate during the development of diseases providing, for example, a tool able to characterise the evolution of cancer. Indeed, our test case concerns the MCF-7 breast cancer cell line, before and after the stimulation with estrogen, since many datasets are available for this case study. In particular, the integration of data about cancer mutations, gene functional annotations, genome conformation, epigenetic patterns, gene expression and metabolic pathways in our multi-layer representation will allow a better interpretation of the mechanisms behind a complex disease such as cancer. Thanks to this multi-layer approach, we focus on the interplay of chromatin conformation and cancer mutations in different pathways, such as metabolic processes, that are very important for tumour development. Working on this model, a variance analysis can be implemented to identify normal variations within each omics and to characterize, by contrast, variations that can be accounted to pathological samples compared to normal ones. This integrative model can be used to identify novel biomarkers and to provide innovative omic-based guidelines for treating many diseases, improving the efficacy of decision trees currently used in clinic.},
	author = {Tordini, Fabio and Aldinucci, Marco and Milanesi, Luciano and Li{\`o}, Pietro and Merelli, Ivan},
	date-modified = {2025-05-24 09:24:06 +0200},
	doi = {10.3389/fgene.2016.00194},
	journal = {Frontiers in Genetics},
	keywords = {bioinformatics},
	number = {194},
	pages = {1--17},
	title = {The Genome Conformation as an Integrator of Multi-Omic Data: The Example of Damage Spreading in Cancer},
	url = {http://journal.frontiersin.org/article/10.3389/fgene.2016.00194},
	volume = {7},
	year = {2016},
	bdsk-url-1 = {http://journal.frontiersin.org/article/10.3389/fgene.2016.00194},
	bdsk-url-2 = {http://dx.doi.org/10.3389/fgene.2016.00194}}

@article{16:shuffle:tpds:misale,
	abstract = {Big data analytics is an indispensable tool in transforming science, engineering, medicine, health-care, finance and ultimately business itself. With the explosion of data sizes and need for shorter time-to-solution, in-memory platforms such as Apache Spark gain increasing popularity. In this context, data shuffling, a particularly difficult transformation pattern, introduces important challenges. Specifically, data shuffling is a key component of complex computations that has a major impact on the overall performance and scalability. Thus, speeding up data shuffling is a critical goal. To this end, state-of-the-art solutions often rely on overlapping the data transfers with the shuffling phase. However, they employ simple mechanisms to decide how much data and where to fetch it from, which leads to sub-optimal performance and excessive auxiliary memory utilization for the purpose of prefetching. The latter aspect is a growing concern, given evidence that memory per computation unit is continuously decreasing while interconnect bandwidth is increasing. This paper contributes a novel shuffle data transfer strategy that addresses the two aforementioned dimensions by dynamically adapting the prefetching to the computation. We implemented this novel strategy in Spark, a popular in-memory data analytics framework. To demonstrate the benefits of our proposal, we run extensive experiments on an HPC cluster with large core count per node. Compared with the default Spark shuffle strategy, our proposal shows: up to 40\% better performance with 50\% less memory utilization for buffering and excellent weak scalability.},
	author = {Nicolae, Bogdan and Costa, Carlos H. A. and Misale, Claudia and Katrinis, Kostas and Park, Yoonho},
	date-modified = {2025-05-24 09:23:01 +0200},
	doi = {10.1109/TPDS.2016.2627558},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	keywords = {system-sw},
	number = {99},
	title = {Leveraging Adaptive I/O to Optimize Collective Data Shuffling Patterns for Big Data Analytics},
	url = {https://iris.unito.it/retrieve/handle/2318/1624908/295954/tpds_4aperto.pdf},
	volume = {PP},
	year = {2016},
	bdsk-url-1 = {http://dx.doi.org/10.1109/TPDS.2016.2627558},
	bdsk-url-2 = {https://iris.unito.it/retrieve/handle/2318/1624908/295954/tpds_4aperto.pdf}}

@inproceedings{CDL16,
	author = {Castellani, Ilaria and Dezani{-}Ciancaglini, Mariangiola and de' Liguoro, Ugo},
	booktitle = {PLACES'16},
	keywords = {rephrase, semantics},
	pages = {1--12},
	series = {EPTCS},
	title = {Secure Multiparty Sessions with Topics},
	url = {http://www.di.unito.it/~dezani/papers/cdl16.pdf},
	volume = {211},
	year = {2016},
	bdsk-url-1 = {http://www.di.unito.it/~dezani/papers/cdl16.pdf}}

@article{CDP16,
	author = {Castellani, Ilaria and Dezani{-}Ciancaglini, Mariangiola and P\'{e}rez, Jorge A.},
	journal = {{Formal Aspects of Computing}},
	keywords = {rephrase, semantics},
	number = {4},
	pages = {669--696},
	publisher = {Springer},
	title = {Self-Adaptation and Secure Information Flow in Multiparty Communications},
	url = {http://www.di.unito.it/~dezani/papers/cdp16.pdf},
	volume = {28},
	year = {2016},
	bdsk-url-1 = {http://www.di.unito.it/~dezani/papers/cdp16.pdf}}

@inproceedings{CDV16,
	author = {Mario Coppo and Mariangiola Dezani{-}Ciancaglini and Betti Venneri},
	booktitle = {PLACES'16},
	keywords = {rephrase, semantics},
	pages = {25--36},
	series = {EPTCS},
	title = {Parallel Monitors for Self-adaptive Sessions},
	url = {http://www.di.unito.it/~dezani/papers/cdv16.pdf},
	volume = {211},
	year = {2016},
	bdsk-url-1 = {http://www.di.unito.it/~dezani/papers/cdv16.pdf}}

@inproceedings{DGJPY16,
	author = {Mariangiola Dezani{-}Ciancaglini and Silvia Ghilezan and Svetlana Jaksic and Jovanka Pantovic and Nobuko Yoshida},
	booktitle = {Theory and Practice of Formal Methods},
	doi = {10.1007/978-3-319-30734-3\_12},
	keywords = {rephrase, semantics},
	pages = {155--172},
	series = {LNCS},
	title = {Denotational and Operational Preciseness of Subtyping: A Roadmap},
	url = {http://www.di.unito.it/~dezani/papers/dgjpy16.pdf},
	volume = {9660},
	year = {2016},
	bdsk-url-1 = {http://www.di.unito.it/~dezani/papers/dgjpy16.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-319-30734-3%5C_12}}

@inproceedings{DG16,
	author = {Mariangiola Dezani{-}Ciancaglini and Paola Giannini},
	booktitle = {EXPRESS/SOS'16},
	keywords = {rephrase, semantics},
	pages = {60--74},
	series = {EPTCS},
	title = {Reversible Multiparty Sessions with Checkpoints},
	url = {http://www.di.unito.it/~dezani/papers/dg16.pdf},
	volume = {222},
	year = {2016},
	bdsk-url-1 = {http://www.di.unito.it/~dezani/papers/dg16.pdf}}

@article{DBLP:journals/ftpl/AnconaBB0CDGGGH16,
  author       = {Davide Ancona and
                  Viviana Bono and
                  Mario Bravetti and
                  Joana Campos and
                  Giuseppe Castagna and
                  Pierre{-}Malo Deni{\'{e}}lou and
                  Simon J. Gay and
                  Nils Gesbert and
                  Elena Giachino and
                  Raymond Hu and
                  Einar Broch Johnsen and
                  Francisco Martins and
                  Viviana Mascardi and
                  Fabrizio Montesi and
                  Rumyana Neykova and
                  Nicholas Ng and
                  Luca Padovani and
                  Vasco T. Vasconcelos and
                  Nobuko Yoshida},
  title        = {Behavioral Types in Programming Languages},
  journal      = {Found. Trends Program. Lang.},
  volume       = {3},
  number       = {2-3},
  pages        = {95--230},
  year         = {2016},
  url          = {https://doi.org/10.1561/2500000031},
  doi          = {10.1561/2500000031},
  timestamp    = {Tue, 21 Mar 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/ftpl/AnconaBB0CDGGGH16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/logcom/Roversi16,
  author       = {Luca Roversi},
  title        = {A deep inference system with a self-dual binder which is complete
                  for linear lambda calculus},
  journal      = {J. Log. Comput.},
  volume       = {26},
  number       = {2},
  pages        = {677--698},
  year         = {2016},
  url          = {https://doi.org/10.1093/logcom/exu033},
  doi          = {10.1093/LOGCOM/EXU033},
  timestamp    = {Sat, 09 Apr 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/logcom/Roversi16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@mastersthesis{tesi:inaudi:15,
	author = {Paolo Inaudi},
	keywords = {fastflow},
	month = oct,
	school = {Computer Science Department, University of Torino},
	title = {Progettazione e sviluppo di un provider libfabric per la rete ad alte prestazioni Ronniee/A3Cube},
	year = {2015}}

@mastersthesis{tesi:viviani:15,
	abstract = {Modern experimental achievements, with LHC results as a prominent but not exclusive representative, have undisclosed a new range of challenges concerning theoretical com- putations. Tree level QED calculation are no more satisfactory due to the very small experimental uncertainty of precision e+ e- measurements, so Next To Leading and Next to Next to Leading Order calculations are required. At the same time many-legs, high-order QCD processes needed to simulate LHC events are raising even more the bar of computational complexity. The drive for the present work has been the interest in calculating high multiplicity Higgs boson processes with a dedicated software library (RECOLA) currently under development at the University of Torino, as well as the related technological challenges. This thesis undertakes the task of exploring the possibilities offered by present and upcoming computing technologies in order to face these challenges properly. The first two chapters outlines the theoretical context and the available technologies. In chapter 3 a a case study is examined in full detail, in order to explore the suitability of different parallel computing solutions. In the chapter 4, some of those solutions are implemented in the context of the RECOLA library, allowing it to handle processes at a previously unexplored scale of complexity. Alongside, the potential of new, cost-effective parallel architectures is tested.},
	author = {Paolo Viviani},
	date-added = {2015-09-27 12:36:54 +0000},
	date-modified = {2015-09-27 13:28:24 +0000},
	keywords = {fastflow,impact},
	school = {Physics Department, University of Torino},
	title = {Parallel Computing Techniques for High Energy Physics},
	year = {2015}}

@inproceedings{opencl:ff:ispa:15,
	abstract = {In this paper we advocate the Loop-of-stencil-reduce pattern as a way to simplify the parallel programming of heterogeneous platforms (multicore+GPUs). Loop-of-Stencil-reduce is general enough to subsume map, reduce, map-reduce, stencil, stencil-reduce, and, crucially, their usage in a loop. It transparently targets (by using OpenCL) combinations of CPU cores and GPUs, and it makes it possible to simplify the deployment of a single stencil computation kernel on different GPUs. The paper discusses the implementation of Loop-of-stencil-reduce within the FastFlow parallel framework, considering a simple iterative data-parallel application as running example (Game of Life) and a highly effective parallel filter for visual data restoration to assess performance. Thanks to the high-level design of the Loop-of-stencil-reduce, it was possible to run the filter seamlessly on a multicore machine, on multi-GPUs, and on both.},
	address = {Helsinki, Finland},
	author = {Aldinucci, Marco and Danelutto, Marco and Drocco, Maurizio and Kilpatrick, Peter and Peretti Pezzi, Guilherme and Torquati, Massimo},
	booktitle = {Proc. of Intl. Workshop on Reengineering for Parallelism in Heterogeneous Parallel Platforms (RePara)},
	date-added = {2015-07-05 09:48:33 +0000},
	date-modified = {2015-09-24 11:14:56 +0000},
	doi = {10.1109/Trustcom.2015.628},
	keywords = {fastflow, repara, HPC},
	month = aug,
	pages = {172--177},
	publisher = {IEEE},
	title = {The Loop-of-Stencil-Reduce paradigm},
	url = {https://iris.unito.it/retrieve/handle/2318/1523738/52857/15_RePara_ISPA.pdf},
	year = {2015},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1523738/52857/15_RePara_ISPA.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/Trustcom.2015.628}}

@article{nuchart:frontiers:15,
	abstract = {The representation, integration and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture (3C) allows the analysis of the chromosome organization in the cell's natural state. While performed genome wide, this technique is usually called Hi-C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi-C data to describe the chromosomal neighbourhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.},
	author = {Merelli, Ivan and Tordini, Fabio and Drocco, Maurizio and Aldinucci, Marco and Li{\`o}, Pietro and Milanesi, Luciano},
	date-added = {2015-02-01 16:38:47 +0000},
	date-modified = {2015-09-24 11:23:10 +0000},
	doi = {10.3389/fgene.2015.00040},
	issn = {1664-8021},
	journal = {Frontiers in Genetics},
	keywords = {bioinformatics, fastflow, bioinformatics},
	number = {40},
	title = {Integrating Multi-omic features exploiting {Chromosome Conformation Capture} data},
	url = {http://journal.frontiersin.org/Journal/10.3389/fgene.2015.00040/pdf},
	volume = {6},
	year = {2015},
	bdsk-url-1 = {http://journal.frontiersin.org/Journal/10.3389/fgene.2015.00040/pdf},
	bdsk-url-2 = {http://dx.doi.org/10.3389/fgene.2015.00040}}

@inproceedings{nuchart:speedup:15,
	abstract = {This paper presents the optimisation efforts on the creation of a graph-based mapping representation of gene adjacency. The method is based on the Hi-C process, starting from Next Generation Sequencing data, and it analyses a huge amount of static data in order to produce maps for one or more genes. Straightforward parallelisation of this scheme does not yield acceptable performance on multicore architectures since the scalability is rather limited due to the memory bound nature of the problem. This work focuses on the memory optimisations that can be applied to the graph construction algorithm and its (complex) data structures to derive a cache-oblivious algorithm and eventually to improve the memory bandwidth utilisation. We used as running example NuChart-II, a tool for annotation and statistic analysis of Hi-C data that creates a gene-centric neighborhood graph. The proposed approach, which is exemplified for Hi-C, addresses several common issue in the parallelisation of memory bound algorithms for multicore. Results show that the proposed approach is able to increase the parallel speedup from 7x to 22x (on a 32-core platform). Finally, the proposed C++ implementation outperforms the first R NuChart prototype, by which it was not possible to complete the graph generation because of strong memory-saturation problems.},
	author = {Drocco, Maurizio and Misale, Claudia and Peretti Pezzi, Guilherme and Tordini, Fabio and Aldinucci, Marco},
	booktitle = {Proc. of 23rd Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-added = {2014-12-03 13:54:08 +0000},
	date-modified = {2017-12-12 14:45:09 +0000},
	doi = {10.1109/PDP.2015.63},
	keywords = {fastflow,bioinformatics, paraphrase, repara, impact},
	month = mar,
	pages = {1--8},
	publisher = {IEEE},
	title = {Memory-Optimised Parallel Processing of {Hi-C} Data},
	url = {https://iris.unito.it/retrieve/handle/2318/1521910/40615/2015_pdp_memopt.pdf},
	year = {2015},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1521910/40615/2015_pdp_memopt.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/PDP.2015.63}}

@inproceedings{nuchar:tool:15,
	abstract = {High-throughput molecular biology techniques are widely used to identify physical interactions between genetic elements located throughout the human genome. Chromosome Conformation Capture (3C) and other related techniques allow to investigate the spatial organisation of chromosomes in the cell's natural state. Recent results have shown that there is a large correlation between co-localization and co-regulation of genes, but these important information are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we introduce NuChart-II, a tool for Hi-C data analysis that provides a gene-centric view of the chromosomal neighbour- hood in a graph-based manner. NuChart-II is an efficient and highly optimized C++ re-implementation of a previous prototype package developed in R. Representing Hi-C data using a graph-based approach overcomes the common view relying on genomic coordinates and permits the use of graph analysis techniques to explore the spatial conformation of a gene neighbourhood.},
	author = {Fabio Tordini and Maurizio Drocco and Claudia Misale and Luciano Milanesi and Pietro Li{\`o} and Ivan Merelli and Marco Aldinucci},
	booktitle = {Proc. of 23rd Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-added = {2014-12-03 13:51:17 +0000},
	date-modified = {2017-12-12 13:55:10 +0000},
	doi = {10.1109/PDP.2015.104},
	keywords = {fastflow, bioinformatics, paraphrase, repara, impact},
	month = mar,
	publisher = {IEEE},
	title = {Parallel Exploration of the Nuclear Chromosome Conformation with {NuChart-II}},
	url = {https://iris.unito.it/retrieve/handle/2318/1522038/40619/2015_pdp_nuchartff.pdf},
	year = {2015},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1522038/40619/2015_pdp_nuchartff.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/PDP.2015.104}}

@inproceedings{14:ff:whatsapp:cibb,
	abstract = {The problem of Haplotype Assembly is an essential step in human genome analysis. It is typically formalised as the Minimum Error Correction (MEC) problem which is NP-hard. MEC has been approached using heuristics, integer linear programming, and fixed-parameter tractability (FPT), including approaches whose runtime is exponential in the length of the DNA fragments obtained by the sequencing process. Technological improvements are currently increasing fragment length, which drastically elevates computational costs for such methods. We present pWhatsHap, a multi-core parallelisation of WhatsHap, a recent FPT optimal approach to MEC. WhatsHap moves complexity from fragment length to fragment overlap and is hence of particular interest when considering sequencing technology's current trends. pWhatsHap further improves the efficiency in solving the MEC problem, as shown by experiments performed on datasets with high coverage.},
	address = {Cambridge, UK},
	author = {Marco Aldinucci and Andrea Bracciali and Tobias Marschall and Murray Patterson and Nadia Pisanti and Massimo Torquati},
	booktitle = {Computational Intelligence Methods for Bioinformatics and Biostatistics - 11th International Meeting, {CIBB} 2014, Cambridge, UK, June 26-28, 2014, Revised Selected Papers},
	date-added = {2014-12-01 23:07:21 +0000},
	date-modified = {2016-08-20 14:15:59 +0000},
	doi = {10.1007/978-3-319-24462-4_21},
	editor = {Clelia Di Serio and Pietro Li{\`{o}} and Alessandro Nonis and Roberto Tagliaferri},
	keywords = {fastflow, bioinformatics},
	pages = {245--258},
	publisher = {Springer},
	series = {{LNCS}},
	title = {High-Performance Haplotype Assembly},
	url = {https://iris.unito.it/retrieve/handle/2318/1523292/46714/2014_pHaplo_cibb.pdf},
	volume = {8623},
	year = {2015},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1523292/46714/2014_pHaplo_cibb.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-319-24462-4_21}}

@article{ff:denoiser:ijhpca:15,
	abstract = {In this paper, a highly effective parallel filter for visual data restoration is presented. The filter is designed following a skeletal approach, using a newly proposed stencil-reduce, and has been implemented by way of the FastFlow parallel programming library. As a result of its high-level design, it is possible to run the filter seamlessly on a multicore machine, on multi-GPGPUs, or on both. The design and implementation of the filter are discussed, and an experimental evaluation is presented.},
	author = {Aldinucci, Marco and Peretti Pezzi, Guilherme and Drocco, Maurizio and Spampinato, Concetto and Torquati, Massimo},
	date-added = {2014-08-23 00:06:10 +0000},
	date-modified = {2015-09-24 11:21:20 +0000},
	doi = {10.1177/1094342014567907},
	journal = {International Journal of High Performance Computing Applications},
	keywords = {fastflow, paraphrase, impact, HPC},
	number = {4},
	pages = {461--472},
	title = {Parallel Visual Data Restoration on Multi-{GPGPUs} using Stencil-Reduce Pattern},
	url = {https://iris.unito.it/retrieve/handle/2318/1522073/299200/ijhpca_4aperto.pdf},
	volume = {29},
	year = {2015},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/1522073/299200/ijhpca_4aperto.pdf},
	bdsk-url-2 = {https://doi.org/10.1177/1094342014567907}}

@inproceedings{14:ff:nuchart:cibb,
	abstract = {Long-range chromosomal associations between genomic regions, and their repositioning in the 3D space of the nucleus, are now considered to be key contributors to the regulation of gene expressions, and important links have been highlighted with other genomic features involved in DNA rearrangements. Recent Chromosome Conformation Capture (3C) measurements performed with high throughput sequencing (Hi-C) and molecular dynamics studies show that there is a large correlation between co-localization and co-regulation of genes, but these important researches are hampered by the lack of biologists-friendly analysis and visualisation software. In this work we present NuChart-II, a software that allows the user to annotate and visualize a list of input genes with information relying on Hi-C data, integrating knowledge data about genomic features that are involved in the chromosome spatial organization. This software works directly with sequenced reads to identify related Hi-C fragments, with the aim of creating gene-centric neighbourhood graphs on which multi-omics features can be mapped. NuChart-II is a highly optimized implementation of a previous prototype package developed in R, in which the graph-based representation of Hi-C data was tested. The prototype showed inevitable problems of scalability while working genome-wide on large datasets: particular attention has been paid in optimizing the data structures employed while constructing the neighbourhood graph, so as to foster an efficient parallel implementation of the software. The normalization of Hi-C data has been modified and improved, in order to provide a reliable estimation of proximity likelihood for the genes.},
	address = {Cambridge, UK},
	author = {Fabio Tordini and Maurizio Drocco and Ivan Merelli and Luciano Milanesi and Pietro Li{\`o} and Marco Aldinucci},
	booktitle = {Proc. of 11th Intl. Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB)},
	date-modified = {2017-12-12 15:19:25 +0000},
	doi = {10.1007/978-3-319-24462-4_25},
	editor = {Clelia Di Serio and Pietro Li{\`{o}} and Alessandro Nonis and Roberto Tagliaferri},
	isbn = {978-3-319-24461-7},
	keywords = {fastflow, bioinformatics, paraphrase, repara, bioinformatics},
	month = jun,
	pages = {298--311},
	publisher = {Springer},
	series = {{LNCS}},
	title = {{NuChart-II}: a graph-based approach for the analysis and interpretation of {Hi-C} data},
	url = {http://calvados.di.unipi.it/storage/paper_files/2014_nuchart_cibb.pdf},
	volume = {8623},
	year = {2015},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2014_nuchart_cibb.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-319-24462-4_25}}

@article{PerettiPezzi201583,
	abstract = {Abstract Optimization of water distribution is a crucial issue which has been targeted by many modeling tools. Useful models, implemented several decades ago, need to be updated and implemented in more powerful computing environments. This paper presents the distributed and redesigned version of a legacy hydraulic simulation software written in Fortran (IRMA) that has been used for over 30 years by the Soci{\'e}t{\'e} du Canal de Provence in order to design and to maintain water distribution networks. \{IRMA\} was developed aiming mainly at the treatment of irrigation networks -- by using the Cl{\'e}ment demand model and is now used to manage more than 6000 km of piped networks. The complexity and size of networks have been growing since the creation of \{IRMA\} and the legacy software could not handle the simulation of very large networks in terms of performance.SAC This limitation has finally imposed to redesign the code by using modern tools and language (Java), and also to run distributed simulations by using the ProActive Parallel Suite.},
	author = {Peretti Pezzi, Guilherme and Vaissi{\'e}, Evelyne and Viala, Yann and Caromel, Denis and Gourbesville, Philippe},
	date-modified = {2021-04-24 23:23:51 +0200},
	doi = {10.1016/j.amc.2015.05.084},
	issn = {0096-3003},
	journal = {Applied Mathematics and Computation},
	keywords = {impact},
	note = {The Fourth European Seminar on Computing (ESCO 2014)},
	pages = {83--95},
	title = {Parallel profiling of water distribution networks using the Cl{\'e}ment formula},
	url = {http://www.sciencedirect.com/science/article/pii/S0096300315007080},
	volume = {267},
	year = {2015},
	bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0096300315007080},
	bdsk-url-2 = {http://dx.doi.org/10.1016/j.amc.2015.05.084}}

@inproceedings{DBLP:conf/cse/SourouriLSBC15,
	author = {Sourouri, Mohammed and Langguth, Johannes and Spiga, Filippo and Baden, Scott B. and Cai, Xing},
	bibsource = {dblp computer science bibliography, http://dblp.org},
	biburl = {http://dblp.org/rec/bib/conf/cse/SourouriLSBC15},
	booktitle = {18th {IEEE} International Conference on Computational Science and Engineering, {CSE} 2015, Porto, Portugal, October 21-23, 2015},
	date-modified = {2025-05-24 09:22:21 +0200},
	doi = {10.1109/CSE.2015.33},
	keywords = {HPC},
	optcrossref = {DBLP:conf/cse/2015},
	pages = {17--26},
	timestamp = {Thu, 15 Jun 2017 21:45:34 +0200},
	title = {{CPU+GPU} Programming of Stencil Computations for Resource-Efficient Use of {GPU} Clusters},
	url = {https://doi.org/10.1109/CSE.2015.33},
	year = {2015},
	bdsk-url-1 = {https://doi.org/10.1109/CSE.2015.33}}

@article{DBLP:journals/cphysics/GlaserNALSMMG15,
	author = {Glaser, Jens and Nguyen, Trung Dac and Anderson, Joshua A. and Lui, Pak and Spiga, Filippo and Millan, Jaime A. and Morse, David C. and Glotzer, Sharon C.},
	bibsource = {dblp computer science bibliography, http://dblp.org},
	biburl = {http://dblp.org/rec/bib/journals/cphysics/GlaserNALSMMG15},
	date-modified = {2025-05-24 09:20:51 +0200},
	doi = {10.1016/j.cpc.2015.02.028},
	journal = {Computer Physics Communications},
	keywords = {HPC},
	pages = {97--107},
	timestamp = {Wed, 14 Jun 2017 20:36:08 +0200},
	title = {Strong scaling of general-purpose molecular dynamics simulations on GPUs},
	url = {https://doi.org/10.1016/j.cpc.2015.02.028},
	volume = {192},
	year = {2015},
	bdsk-url-1 = {https://doi.org/10.1016/j.cpc.2015.02.028}}

@article{DBLP:journals/scp/CanaveseCOPR15,
  author       = {Daniele Canavese and
                  Emanuele Cesena and
                  Rachid Ouchary and
                  Marco Pedicini and
                  Luca Roversi},
  title        = {Light combinators for finite fields arithmetic},
  journal      = {Sci. Comput. Program.},
  volume       = {111},
  pages        = {365--394},
  year         = {2015},
  url          = {https://doi.org/10.1016/j.scico.2015.04.001},
  doi          = {10.1016/J.SCICO.2015.04.001},
  timestamp    = {Thu, 23 Jun 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/scp/CanaveseCOPR15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/types/PaoliniPR15,
  author       = {Luca Paolini and
                  Mauro Piccolo and
                  Luca Roversi},
  editor       = {Tarmo Uustalu},
  title        = {A Certified Study of a Reversible Programming Language},
  booktitle    = {21st International Conference on Types for Proofs and Programs, {TYPES}
                  2015, Tallinn, Estonia, May 18-21, 2015},
  series       = {LIPIcs},
  volume       = {69},
  pages        = {7:1--7:21},
  publisher    = {Schloss Dagstuhl - Leibniz-Zentrum f{\"{u}}r Informatik},
  year         = {2015},
  url          = {https://doi.org/10.4230/LIPIcs.TYPES.2015.7},
  doi          = {10.4230/LIPICS.TYPES.2015.7},
  timestamp    = {Fri, 21 Nov 2025 14:06:54 +0100},
  biburl       = {https://dblp.org/rec/conf/types/PaoliniPR15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:journals/entcs/PaoliniPR16,
  author       = {Luca Paolini and
                  Mauro Piccolo and
                  Luca Roversi},
  editor       = {Pierluigi Crescenzi and
                  Michele Loreti},
  title        = {A Class of Reversible Primitive Recursive Functions},
  booktitle    = {Proceedings of the 16th Italian Conference on Theoretical Computer
                  Science, {ICTCS} 2015, Firenze, Italy, September 9-11, 2015},
  series       = {Electronic Notes in Theoretical Computer Science},
  volume       = {322},
  pages        = {227--242},
  publisher    = {Elsevier},
  year         = {2015},
  url          = {https://doi.org/10.1016/j.entcs.2016.03.016},
  doi          = {10.1016/J.ENTCS.2016.03.016},
  timestamp    = {Thu, 10 Nov 2022 11:36:06 +0100},
  biburl       = {https://dblp.org/rec/journals/entcs/PaoliniPR16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/europar/AntelmiCSV15,
  author       = {Alessia Antelmi and
                  Gennaro Cordasco and
                  Carmine Spagnuolo and
                  Luca Vicidomini},
  editor       = {Sascha Hunold and
                  Alexandru Costan and
                  Domingo Gim{\'{e}}nez and
                  Alexandru Iosup and
                  Laura Ricci and
                  Mar{\'{\i}}a Engracia G{\'{o}}mez Requena and
                  Vittorio Scarano and
                  Ana Lucia Varbanescu and
                  Stephen L. Scott and
                  Stefan Lankes and
                  Josef Weidendorfer and
                  Michael Alexander},
  title        = {On Evaluating Graph Partitioning Algorithms for Distributed Agent
                  Based Models on Networks},
  booktitle    = {Euro-Par 2015: Parallel Processing Workshops - Euro-Par 2015 International
                  Workshops, Vienna, Austria, August 24-25, 2015, Revised Selected Papers},
  series       = {Lecture Notes in Computer Science},
  volume       = {9523},
  pages        = {367--378},
  publisher    = {Springer},
  year         = {2015},
  url          = {https://doi.org/10.1007/978-3-319-27308-2\_30},
  doi          = {10.1007/978-3-319-27308-2\_30},
  timestamp    = {Thu, 07 May 2026 20:26:54 +0200},
  biburl       = {https://dblp.org/rec/conf/europar/AntelmiCSV15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{cloud:patat:14,
	address = {York, United Kingdom},
	author = {Epitropakis, Michael G. and Bracciali, Andrea and Aldinucci, Marco and Potts, Emily and Burke, Edmund K.},
	booktitle = {Proc. of 10th Intl. Conference on the Practice and Theory of Automated Timetabling},
	date-added = {2015-03-15 14:34:03 +0000},
	date-modified = {2021-03-26 23:55:53 +0100},
	doi = {978-0-9929984-0-0},
	editor = {Ender {\"O}zcan and Edmund K. Burke and Barry MCCollum},
	month = aug,
	publisher = {PATAT},
	title = {Predictive scheduling for optimal cloud configuration},
	url = {http://www.patatconference.org/patat2014/proceedings/3_12.pdf},
	year = {2014},
	bdsk-url-1 = {http://www.patatconference.org/patat2014/proceedings/3_12.pdf}}

@article{cwc:cloud:bib:13,
	abstract = {The stochastic modelling of biological systems, coupled with Monte Carlo simulation of models, is an increasingly popular technique in bioinformatics. The simulation-analysis workflow may result computationally expensive reducing the interactivity required in the model tuning. In this work, we advocate the high-level software design as a vehicle for building efficient and portable parallel simulators for the cloud. In particular, the Calculus of Wrapped Components (CWC) simulator for systems biology, which is designed according to the FastFlow pattern-based approach, is presented and discussed. Thanks to the FastFlow framework, the CWC simulator is designed as a high-level workflow that can simulate CWC models, merge simulation results and statistically analyse them in a single parallel workflow in the cloud. To improve interactivity, successive phases are pipelined in such a way that the workflow begins to output a stream of analysis results immediately after simulation is started. Performance and effectiveness of the CWC simulator are validated on the Amazon Elastic Compute Cloud.},
	author = {Marco Aldinucci and Massimo Torquati and Concetto Spampinato and Maurizio Drocco and Claudia Misale and Cristina Calcagno and Mario Coppo},
	date-added = {2014-12-21 17:49:54 +0000},
	date-modified = {2021-04-24 23:24:25 +0200},
	doi = {10.1093/bib/bbt040},
	issn = {1467-5463},
	journal = {Briefings in Bioinformatics},
	keywords = {fastflow, bioinformatics, paraphrase, impact},
	number = {5},
	pages = {798--813},
	title = {Parallel stochastic systems biology in the cloud},
	url = {https://iris.unito.it/retrieve/handle/2318/140080/22528/FF_Cloud_briefings_final_submitted_copy.pdf},
	volume = {15},
	year = {2014},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/140080/22528/FF_Cloud_briefings_final_submitted_copy.pdf},
	bdsk-url-2 = {https://doi.org/10.1093/bib/bbt040}}

@article{biocloud:ccpe:13,
	abstract = {In this paper, we present a bioinformatics knowledge discovery tool for extracting and validating associations between biological entities. By mining specialised scientific literature, the tool not only generates biological hypotheses in the form of associations between genes, proteins, miRNA and diseases, but also validates the plausibility of such associations against high-throughput biological data (e.g. microarray) and annotated databases (e.g. Gene Ontology). Both the knowledge discovery system and its validation are carried out by exploiting the advantages and the potentialities of the Cloud, which allowed us to derive and check the validity of thousands of biological associations in a reasonable amount of time. The system was tested on a dataset containing more than 1000 gene-disease associations achieving an average recall of about 71\%, outperforming existing approaches. The results also showed that porting a data-intensive application in an IaaS cloud environment boosts significantly the application's efficiency.},
	author = {Concetto Spampinato and Isaak Kavasidis and Marco Aldinucci and Carmelo Pino and Daniela Giordano and Alberto Faro},
	date-added = {2014-12-21 17:48:24 +0000},
	date-modified = {2025-05-24 09:22:31 +0200},
	doi = {10.1002/cpe.3130},
	journal = {Concurrency and Computation: Practice and Experience},
	keywords = {bioinformatics},
	number = {10},
	pages = {1771--1786},
	title = {Discovering Biological Knowledge by Integrating High Throughput Data and Scientific Literature on the Cloud},
	url = {https://iris.unito.it/retrieve/handle/2318/139542/22526/2013_biocloud_ccpe.pdf},
	volume = {26},
	year = {2014},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/139542/22526/2013_biocloud_ccpe.pdf},
	bdsk-url-2 = {https://doi.org/10.1002/cpe.3130}}

@article{ijpp:patterns:13,
	abstract = {Structured parallel programming is recognised as a viable and effective means of tackling parallel programming problems. Recently, a set of simple and powerful parallel building blocks (RISC-pb2l) has been proposed to support modelling and implementation of parallel frameworks. In this work we demonstrate how that same parallel building block set may be used to model both general purpose parallel programming abstractions, not usually listed in classical skeleton sets, and more specialized domain specific parallel patterns. We show how an implementation of RISC-pb2l can be realised via the FastFlow framework and present experimental evidence of the feasibility and efficiency of the approach.},
	author = {Marco Aldinucci and Sonia Campa and Marco Danelutto and Peter Kilpatrick and Massimo Torquati},
	date-added = {2014-12-21 17:47:21 +0000},
	date-modified = {2015-09-27 12:32:37 +0000},
	doi = {10.1007/s10766-013-0273-6},
	issn = {0885-7458},
	journal = {International Journal of Parallel Programming},
	keywords = {fastflow, paraphrase},
	number = {6},
	pages = {1012--1031},
	title = {Design patterns percolating to parallel programming framework implementation},
	url = {https://iris.unito.it/retrieve/handle/2318/140069/22527/2013_ijpp_patterns-web_4aperto_1238811.pdf},
	volume = {42},
	year = {2014},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/140069/22527/2013_ijpp_patterns-web_4aperto_1238811.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/s10766-013-0273-6}}

@article{yadtff:ccpe:13,
	abstract = {The whole computer hardware industry embraced multi-core. The extreme optimisation of sequential algorithms is then no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an in-depth study of the parallelisation of an implementation of the C4.5 algorithm for multi-core architectures. We characterise elapsed time lower bounds for the forms of parallelisations adopted, and achieve close to optimal performances. Our implementation is based on the FastFlow parallel programming environment and it requires minimal changes to the original sequential code.},
	author = {Marco Aldinucci and Salvatore Ruggieri and Massimo Torquati},
	date-added = {2014-12-21 17:46:33 +0000},
	date-modified = {2015-09-27 12:17:52 +0000},
	doi = {10.1002/cpe.3063},
	journal = {Concurrency and Computation: Practice and Experience},
	keywords = {fastflow, paraphrase},
	number = {3},
	pages = {800--820},
	title = {Decision Tree Building on Multi-Core using FastFlow},
	url = {https://iris.unito.it/retrieve/handle/2318/139522/118602/yadtff-j.pdf},
	volume = {26},
	year = {2014},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/139522/118602/yadtff-j.pdf},
	bdsk-url-2 = {https://doi.org/10.1002/cpe.3063}}

@article{cwcsim:ff:multicore:biomed:14,
	abstract = {The paper arguments are on enabling methodologies for the design of a fully parallel, online, interactive tool aiming to support the bioinformatics scientists .In particular, the features of these methodologies, supported by the FastFlow parallel programming framework, are shown on a simulation tool to perform the modeling, the tuning, and the sensitivity analysis of stochastic biological models. A stochastic simulation needs thousands of independent simulation trajectories turning into big data that should be analysed by statistic and data mining tools. In the considered approach the two stages are pipelined in such a way that the simulation stage streams out the partial results of all simulation trajectories to the analysis stage that immediately produces a partial result. The simulation-analysis workflow is validated for performance and effectiveness of the online analysis in capturing biological systems behavior on a multicore platform and representative proof-of-concept biological systems. The exploited methodologies include pattern-based parallel programming and data streaming that provide key features to the software designers such as performance portability and efficient in-memory (big) data management and movement. Two paradigmatic classes of biological systems exhibiting multistable and oscillatory behavior are used as a testbed.},
	author = {Marco Aldinucci and Cristina Calcagno and Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Eva Sciacca and Salvatore Spinella and Massimo Torquati and Angelo Troina},
	date-added = {2014-06-26 21:30:32 +0000},
	date-modified = {2015-09-27 12:17:05 +0000},
	doi = {10.1155/2014/207041},
	journal = {BioMed Research International},
	keywords = {fastflow, bioinformatics, paraphrase},
	title = {On designing multicore-aware simulators for systems biology endowed with on-line statistics},
	url = {http://downloads.hindawi.com/journals/bmri/2014/207041.pdf},
	year = {2014},
	bdsk-url-1 = {http://downloads.hindawi.com/journals/bmri/2014/207041.pdf},
	bdsk-url-2 = {https://doi.org/10.1155/2014/207041}}

@inproceedings{ff:gtc:2014,
	abstract = {Learn how FastFlow's parallel patterns can be used to design parallel applications for execution on both CPUs and GPGPUs while avoiding most of the complex low-level detail needed to make them efficient, portable and rapid to prototype. As use case, we will show the design and effectiveness of a novel universal image filtering template based on the variational approach.},
	address = {San Jose, CA, USA},
	author = {Aldinucci, Marco and Torquati, Massimo and Drocco, Maurizio and Peretti Pezzi, Guilherme and Spampinato, Concetto},
	booktitle = {GPU Technology Conference (GTC)},
	date-added = {2014-04-19 12:52:40 +0000},
	date-modified = {2017-12-12 13:54:25 +0000},
	keywords = {fastflow, HPC, impact, paraphrase},
	month = mar,
	title = {FastFlow: Combining Pattern-Level Abstraction and Efficiency in {GPGPUs}},
	url = {http://calvados.di.unipi.it/storage/talks/2014_S4729-Marco-Aldinucci.pdf},
	year = {2014},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/talks/2014_S4729-Marco-Aldinucci.pdf}}

@inproceedings{cwc:gpu:dcperf:14,
	abstract = {The stochastic modelling of biological systems, cou- pled with Monte Carlo simulation of models, is an increasingly popular technique in Bioinformatics. The simulation-analysis workflow may result into a computationally expensive task reducing the interactivity required in the model tuning. In this work, we advocate high-level software design as a vehicle for building efficient and portable parallel simulators for a variety of platforms, ranging from multi-core platforms to GPGPUs to cloud. In particular, the Calculus of Wrapped Compartments (CWC) parallel simulator for systems biology equipped with on- line mining of results, which is designed according to the FastFlow pattern-based approach, is discussed as a running example. In this work, the CWC simulator is used as a paradigmatic example of a complex C++ application where the quality of results is correlated with both computation and I/O bounds, and where high-quality results might turn into big data. The FastFlow parallel programming framework, which advocates C++ pattern- based parallel programming makes it possible to develop portable parallel code without relinquish neither run-time efficiency nor performance tuning opportunities. Performance and effectiveness of the approach are validated on a variety of platforms, inter-alia cache-coherent multi-cores, cluster of multi-core (Ethernet and Infiniband) and the Amazon Elastic Compute Cloud.},
	address = {Madrid, Spain},
	author = {Aldinucci, Marco and Drocco, Maurizio and Peretti Pezzi, Guilherme and Misale, Claudia and Tordini, Fabio and Torquati, Massimo},
	booktitle = {Proc. of 34th IEEE Intl. Conference on Distributed Computing Systems Workshops (ICDCSW)},
	date-added = {2014-04-19 12:44:39 +0000},
	date-modified = {2021-04-24 23:23:10 +0200},
	doi = {10.1109/ICDCSW.2014.38},
	keywords = {fastflow, bioinformatics, paraphrase, impact},
	publisher = {IEEE},
	title = {Exercising high-level parallel programming on streams: a systems biology use case},
	url = {https://iris.unito.it/retrieve/handle/2318/154516/26657/2014_dcperf_cwc_gpu.pdf},
	year = {2014},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/154516/26657/2014_dcperf_cwc_gpu.pdf},
	bdsk-url-2 = {https://doi.org/10.1109/ICDCSW.2014.38}}

@inproceedings{ff:video:hlpgpu:14,
	abstract = {In this paper, a highly-effective parallel filter for video denoising is presented. The filter is designed using a skeletal approach, and has been implemented by way of the FastFlow parallel programming library. As a result of its high-level design, it is possible to run the filter seamlessly on a multi-core machine, on GPGPU(s), or on both. The design and the implementation of the filter are discussed, and an experimental evaluation is presented. Various mappings of the filtering stages are comparatively discussed.},
	author = {Aldinucci, Marco and Peretti Pezzi, Guilherme and Drocco, Maurizio and Tordini, Fabio and Kilpatrick, Peter and Torquati, Massimo},
	booktitle = {Proc. of Intl. Workshop on High-level Programming for Heterogeneous and Hierarchical Parallel Systems (HLPGPU)},
	date-added = {2013-12-07 18:28:32 +0000},
	date-modified = {2015-09-27 12:42:02 +0000},
	keywords = {fastflow, paraphrase, impact},
	title = {Parallel video denoising on heterogeneous platforms},
	url = {http://calvados.di.unipi.it/storage/paper_files/2014_ff_video_denoiser_hlpgpu.pdf},
	year = {2014},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2014_ff_video_denoiser_hlpgpu.pdf}}

@inproceedings{ff:bowtie2:pdp:14,
	abstract = {The implementation of DNA alignment tools for Bioinformatics lead to face different problems that dip into performances. A single alignment takes an amount of time that is not predictable and there are different factors that can affect performances, for instance the length of sequences can determine the computational grain of the task and mismatches or insertion/deletion (indels) increase time needed to complete an alignment. Moreover, an alignment is a strong memory- bound problem because of the irregular memory access pat- terns and limitations in memory-bandwidth. Over the years, many alignment tools were implemented. A concrete example is Bowtie2, one of the fastest (concurrent, Pthread-based) and state of the art not GPU-based alignment tool. Bowtie2 exploits concurrency by instantiating a pool of threads, which have access to a global input dataset, share the reference genome and have access to different objects for collecting alignment results. In this paper a modified implementation of Bowtie2 is presented, in which the concurrency structure has been changed. The proposed implementation exploits the task-farm skeleton pattern implemented as a Master-Worker. The Master-Worker pattern permits to delegate only to the Master thread dataset reading and to make private to each Worker data structures that are shared in the original version. Only the reference genome is left shared. As a further optimisation, the Master and each Worker were pinned on cores and the reference genome was allocated interleaved among memory nodes. The proposed implementation is able to gain up to 10 speedup points over the original implementation.},
	address = {Torino, Italy},
	author = {Claudia Misale},
	booktitle = {Proc. of Intl. Euromicro PDP 2014: Parallel Distributed and network-based Processing},
	date-added = {2013-12-07 18:25:55 +0000},
	date-modified = {2015-09-27 12:41:24 +0000},
	doi = {10.1109/PDP.2014.50},
	editor = {Marco Aldinucci and Daniele D'Agostino and Peter Kilpatrick},
	keywords = {fastflow, paraphrase},
	note = {(Best paper award)},
	publisher = {IEEE},
	title = {Accelerating Bowtie2 with a lock-less concurrency approach and memory affinity},
	url = {http://calvados.di.unipi.it/storage/paper_files/2014_pdp_bowtieff.pdf},
	year = {2014},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2014_pdp_bowtieff.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/PDP.2014.50}}

@inproceedings{ff:infiniband:pdp:14,
	abstract = {InfiniBand networks are commonly used in the high performance computing area. They offer RDMA-based opera- tions that help to improve the performance of communication subsystems. In this paper, we propose a minimal message-passing communication layer providing the programmer with a point-to- point communication channel implemented by way of InfiniBand RDMA features. Differently from other libraries exploiting the InfiniBand features, such as the well-known Message Passing Interface (MPI), the proposed library is a communication layer only rather than a programming model, and can be easily used as building block for high-level parallel programming frameworks. Evaluated on micro-benchmarks, the proposed RDMA-based communication channel implementation achieves a comparable performance with highly optimised MPI/InfiniBand implemen- tations. Eventually, the flexibility of the communication layer is evaluated by integrating it within the FastFlow parallel frame- work, currently supporting TCP/IP networks (via the ZeroMQ communication library).},
	address = {Torino, Italy},
	author = {Secco, Alessandro and Uddin, Irfan and Peretti Pezzi, Guilherme and Torquati, Massimo},
	booktitle = {Proc. of Intl. Euromicro PDP 2014: Parallel Distributed and network-based Processing},
	date-added = {2013-12-07 18:22:35 +0000},
	date-modified = {2015-09-27 12:35:04 +0000},
	doi = {10.1109/PDP.2014.23},
	editor = {Marco Aldinucci and Daniele D'Agostino and Peter Kilpatrick},
	keywords = {fastflow, paraphrase, impact},
	publisher = {IEEE},
	title = {Message passing on InfiniBand {RDMA} for parallel run-time supports},
	url = {https://iris.unito.it/retrieve/handle/2318/151178/690885/2014_ff_infiniband_pdp.pdf},
	year = {2014},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2014_ff_infiniband_pdp.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/PDP.2014.23}}

@inproceedings{ff:ffmdf:pdcn:14,
	author = {Buono, Daniele and Danelutto, Marco and De Matteis, Tiziano and Mencagli, Gabriele and Torquati, Massimo},
	booktitle = {Proc. of the 12th International Conference on Parallel and Distributed Computing and Networks (PDCN 2014)},
	date-modified = {2018-12-27 18:52:39 +0100},
	keywords = {fastflow},
	month = feb,
	publisher = {IASTED, ACTA press},
	title = {A Lightweight Run-Time Support For Fast Dense Linear Algebra on Multi-Core},
	year = {2014}}

@inproceedings{ff:acaces:14,
	abstract = {Modern computers are built upon heterogeneous multi-core/many cores architectures (e.g. GPGPU connected to multi-core CPU). Achieving peak performance on these architectures is hard and may require a substantial programming effort. High-level programming patterns, coupled with efficient low-level runtime supports, have been proposed to relieve the programmer from worrying about low-level details such as synchronisation of racing processes as well as those fine tunings needed to improve the overall performance. Among them are (parallel) dynamic memory allocation and effective exploitation of the memory hierarchy. The memory allocator is often a bottleneck that severely limits program scalability, robustness and portability on parallel systems. In this work we introduce a novel memory allocator, based on the FastFlow's allocator and the recently proposed CUDA Unified Memory, which aims to efficiently integrate host and device memories into a unique dynamic-allocable memory space, accessible transparently by both host and device code.},
	address = {Fiuggi, Italy},
	author = {Maurizio Drocco and Marco Aldinucci and Massimo Torquati},
	booktitle = {Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) -- Poster Abstracts},
	date-modified = {2016-08-20 17:29:47 +0000},
	keywords = {fastflow, HPC},
	publisher = {HiPEAC},
	title = {A Dynamic Memory Allocator for heterogeneous platforms},
	url = {http://calvados.di.unipi.it/storage/paper_files/2014_ACACES_ex-abstract.pdf},
	year = {2014},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2013_ACACES_ex-abstract.pdf},
	bdsk-url-2 = {http://calvados.di.unipi.it/storage/paper_files/2014_ACACES_ex-abstract.pdf}}

@article{bowtie-bwa:ff:multicore:biomed:14,
	abstract = {In this paper we advocate high-level programming methodology for Next Generation Sequencers (NGS) alignment tools for both productivity and absolute performance. We analyse the problem of parallel alignment and review the parallelisation strategies of the most popular alignment tools, which can all be abstracted to a single parallel paradigm. We compare these tools against their porting onto the FastFlow pattern-based programming framework, which provides programmers with high-level parallel patterns. By using a high-level approach, programmers are liberated from all complex aspects of parallel programming, such as synchronisation protocols and task scheduling, gaining more possibility for seamless performance tuning. In this work we show some use case in which, by using a high-level approach for parallelising NGS tools, it is possible to obtain comparable or even better absolute performance for all used datasets.},
	author = {Claudia Misale and Giulio Ferrero and Massimo Torquati and Marco Aldinucci},
	date-added = {2013-01-15 15:55:59 +0000},
	date-modified = {2015-09-27 12:16:28 +0000},
	doi = {10.1155/2014/539410},
	journal = {BioMed Research International},
	keywords = {fastflow,bioinformatics, paraphrase, repara},
	title = {Sequence alignment tools: one parallel pattern to rule them all?},
	url = {http://downloads.hindawi.com/journals/bmri/2014/539410.pdf},
	year = {2014},
	bdsk-url-1 = {http://downloads.hindawi.com/journals/bmri/2014/539410.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1155/2014/539410}}

@inproceedings{ff:gtc:2014:short,
	abstract = {Get an overview of FastFlow's parallel patterns can be used to design parallel applications for execution on both CPUs and GPGPUs while avoiding most of the complex low-level detail needed to make them efficient, portable and rapid to prototype. For a more detailed and technical review of FastFlow's parallel patterns as well as a use case where we will show the design and effectiveness of a novel universal image filtering template based on the variational approach.},
	address = {San Jose, CA, USA},
	author = {Aldinucci, Marco and Torquati, Massimo and Drocco, Maurizio and Peretti Pezzi, Guilherme and Spampinato, Concetto},
	booktitle = {GPU Technology Conference (GTC)},
	date-added = {2014-04-13 23:20:52 +0000},
	date-modified = {2017-12-12 13:54:20 +0000},
	keywords = {fastflow, HPC, impact, paraphrase},
	month = mar,
	title = {An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in {GPGPUs}},
	url = {http://calvados.di.unipi.it/storage/talks/2014_S4585-Marco-Aldinucci.pdf},
	year = {2014},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/talks/2014_S4585-Marco-Aldinucci.pdf}}

@inproceedings{2014:ff:pool:hlpp,
	abstract = {We introduce a new parallel pattern derived from a specific application domain and show how it turns out to have application beyond its domain of origin. The pool evolution pattern models the parallel evolution of a population subject to mutations and evolving in such a way that a given fitness function is optimized. The pattern has been demonstrated to be suitable for capturing and modeling the parallel patterns underpinning various evolutionary algorithms, as well as other parallel patterns typical of symbolic computation. In this paper we introduce the pattern, developed in the framework of the ParaPhrase EU-funded FP7 project, we discuss its implementation on modern multi/many core architectures and finally present experimental results obtained with FastFlow and Erlang implementations to assess its feasibility and scalability.},
	address = {Amsterdam, The Netherlands},
	author = {Marco Aldinucci and Sonia Campa and Marco Danelutto and Peter Kilpatrick and Massimo Torquati},
	booktitle = {Proc.of the 7th Intl. Symposium on High-level Parallel Programming and Applications (HLPP)},
	date-modified = {2015-09-27 12:14:30 +0000},
	keywords = {fastflow, paraphrase, repara},
	month = jul,
	title = {Pool evolution: a domain specific parallel pattern},
	url = {http://calvados.di.unipi.it/storage/paper_files/2014_hlpp_pool.pdf},
	year = {2014},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2014_hlpp_pool.pdf}}

@inproceedings{DBLP:conf/pppj/BonoMN14,
  author       = {Viviana Bono and
                  Enrico Mensa and
                  Marco Naddeo},
  editor       = {Joanna Kolodziej and
                  Bruce R. Childers},
  title        = {Trait-oriented programming in Java 8},
  booktitle    = {2014 International Conference on Principles and Practices of Programming
                  on the Java Platform Virtual Machines, Languages and Tools, {PPPJ}
                  '14, Cracow, Poland, September 23-26, 2014},
  pages        = {181--186},
  publisher    = {{ACM}},
  year         = {2014},
  url          = {https://doi.org/10.1145/2647508.2647520},
  doi          = {10.1145/2647508.2647520},
  timestamp    = {Sat, 19 Oct 2019 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/pppj/BonoMN14.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:journals/corr/BenkeBS15,
  author       = {Marcin Benke and
                  Viviana Bono and
                  Aleksy Schubert},
  editor       = {Jakob Rehof},
  title        = {Lucretia - intersection type polymorphism for scripting languages},
  booktitle    = {Proceedings Seventh Workshop on Intersection Types and Related Systems,
                  {ITRS} 2014, Vienna, Austria, 18 July 2014},
  series       = {{EPTCS}},
  volume       = {177},
  pages        = {65--78},
  year         = {2014},
  url          = {https://doi.org/10.4204/EPTCS.177.6},
  doi          = {10.4204/EPTCS.177.6},
  timestamp    = {Tue, 21 Mar 2023 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/BenkeBS15.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{ff:acaces:13,
	abstract = {The diffusion of the Next Generation Sequencing (NGS) has increased the amount of data obtainable by genomic experiments. From a DNA sample a NGS run is able to produce millions of short sequences (called reads), which should be mapped into a reference genome. In this paper, we analyse the performance of Bowtie2, a fast and popular DNA mapping tool. Bowtie2 exhibits a multithreading implementation on top of pthreads, spin-locks and SSE2 SIMD extension. From parallel computing viewpoint, is a paradigmatic example of a software requiring to address three fundamental problems in shared-memory programming for cache-coherent multi-core platforms: synchronisation efficiency at very fine grain (due to short reads), load-balancing (due to long reads), and efficient usage of memory subsystem (due to SSE2 memory pressure). We compare the original implementation against an alternative implementation on top of the FastFlow pattern-based programming framework. The proposed design exploits the high-level farm pattern of FastFlow, which is implemented top of nonblocking multi-threading and lock-less (CAS-free) queues, and provides the programmer with high-level mechanism to tune task scheduling to achieve both load-balancing and memory affinity. The proposed design, despite the high-level  design, is always faster and more scalable with respect to the original one. The design of both original and alternative version will be presented along with their experimental evaluation on real-world data sets.},
	address = {Fiuggi, Italy},
	author = {Claudia Misale and Marco Aldinucci and Massimo Torquati},
	booktitle = {Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) -- Poster Abstracts},
	date-added = {2015-03-21 15:12:59 +0000},
	date-modified = {2015-03-21 15:12:59 +0000},
	isbn = {9789038221908},
	keywords = {fastflow},
	publisher = {HiPEAC},
	title = {Memory affinity in multi-threading: the Bowtie2 case study},
	url = {https://iris.unito.it/retrieve/handle/2318/143005/23874/2013_ACACES_ex-abstract.pdf},
	year = {2013},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/143005/23874/2013_ACACES_ex-abstract.pdf}}

@mastersthesis{tesi:drocco:13,
	abstract = {The stochastic simulation of biological systems is an increasingly popular technique in bioinformatics. It is often an enlightening technique, especially for multi-stable systems whose dynamics can be hardly captured with ordinary differential equations. To be effective, stochastic simulations should be supported by powerful statistical analysis tools. The simulation/analysis workflow may however result in being computationally expensive, thus compromising the interactivity required especially in model tuning. In this work we discuss the main opportunities to speed up the framework by parallelisation on modern multicore and hybrid multicore and distributed platforms, advocating the high-level design of simulators for stochastic systems as a vehicle for building efficient and portable parallel simulators endowed with on-line statistical analysis. In particular, the Calculus of Wrapped Compartments (CWC) Simulator, which is designed according to the FastFlow's pattern-based approach, is presented and discussed in this work.},
	author = {Maurizio Drocco},
	date-modified = {2013-11-24 00:29:54 +0000},
	keywords = {fastflow},
	month = jul,
	school = {Computer Science Department, University of Torino, Italy},
	title = {Parallel stochastic simulators in systems biology: the evolution of the species},
	url = {http://calvados.di.unipi.it/storage/paper_files/2013_tesi_drocco.pdf},
	year = {2013},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2013_tesi_drocco.pdf}}

@incollection{adaptivity:fmco:11,
	abstract = {The management of non-functional features (performance, security, power management, etc.) is traditionally a difficult, error prone task for programmers of parallel applications. To take care of these non-functional features, autonomic managers running policies represented as rules using sensors and actuators to monitor and transform a running parallel application may be used. We discuss an approach aimed at providing formal tool support to the integration of independently developed autonomic managers taking care of different non-functional concerns within the same parallel application. Our approach builds on the Behavioural Skeleton experience (autonomic management of non-functional features in structured parallel applications) and on previous results on conflict detection and resolution in rule-based systems.},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Carlo Montangero and Laura Semini},
	booktitle = {Formal Methods for Components and Objects: Intl. Symposium, FMCO 2011, Torino, Italy, October 3-5, 2011, Revised Invited Lectures},
	date-added = {2012-06-04 19:05:16 +0200},
	date-modified = {2021-04-24 23:22:33 +0200},
	doi = {10.1007/978-3-642-35887-6_11},
	editor = {Bernhard Beckert and Ferruccio Damiani and Frank S. de Boer and Marcello M. Bonsangue},
	isbn = {978-3-642-35886-9},
	keywords = {paraphrase},
	pages = {199--217},
	publisher = {Springer},
	series = {LNCS},
	title = {Managing Adaptivity in Parallel Systems},
	url = {http://calvados.di.unipi.it/storage/paper_files/2013_fmco11_adaptivity.pdf},
	volume = {7542},
	year = {2013},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2013_fmco11_adaptivity.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-35887-6_11}}

@inproceedings{annotation:para:12,
	abstract = {We describe an approach aimed at addressing the issue of joint exploitation of control (stream) and data parallelism in a skele-ton based parallel programming environment, based on annotations and refactoring. Annotations drive efficient implementation of a parallel com-putation. Refactoring is used to transform the associated skeleton tree into a more efficient, functionally equivalent skeleton tree. In most cases,cost models are used to drive the refactoring process. We show howsample use case applications/kernels may be optimized and discuss pre-liminary experiments with FastFlow assessing the theoretical results.},
	author = {Marco Aldinucci and Sonia Campa and Peter Kilpatrick and Massimo Torquati},
	booktitle = {Euro-Par 2012 Workshops, Proc. of the ParaPhrase Workshop on Parallel Processing},
	date-added = {2012-07-23 21:22:03 +0000},
	date-modified = {2015-09-27 12:49:52 +0000},
	doi = {10.1007/978-3-642-36949-0_42},
	keywords = {fastflow, paraphrase},
	pages = {381--390},
	publisher = {Springer},
	series = {LNCS},
	title = {Structured Data Access Annotations for Massively Parallel Computations},
	url = {http://calvados.di.unipi.it/storage/paper_files/2013_annot_europar_workshops.pdf},
	volume = {7640},
	year = {2013},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2013_annot_europar_workshops.pdf},
	bdsk-url-2 = {https://doi.org/10.1007/978-3-642-36949-0_42}}

@inproceedings{ff:distr:cgs:12,
	abstract = {FastFlow is a structured parallel programming framework targeting shared memory multi-core architectures. In this paper we introduce a FastFlow extension aimed at supporting a network of multi-core workstation as well. The extension supports the execution of FastFlow programs by coordinating -- in a structured way -- the fine grain parallel activities running on a single workstation. We discuss the design and the implementation of this extension presenting preliminary experimental results validating it on state-of-the-art networked multi-core nodes.},
	author = {Marco Aldinucci and Sonia Campa and Marco Danelutto and Peter Kilpatrick and Massimo Torquati},
	booktitle = {Euro-Par 2012 Workshops, Proc. of the CoreGrid Workshop on Grids, Clouds and P2P Computing},
	date-added = {2012-07-23 21:22:03 +0000},
	date-modified = {2015-09-27 12:47:54 +0000},
	doi = {10.1007/978-3-642-36949-0_7},
	keywords = {fastflow, paraphrase},
	pages = {47--56},
	publisher = {Springer},
	series = {LNCS},
	title = {Targeting Distributed Systems in FastFlow},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_distr_ff_cgsymph.pdf},
	volume = {7640},
	year = {2013},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2012_distr_ff_cgsymph.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-36949-0_7}}

@inproceedings{ff_cwc_distr:pdp:13,
	abstract = {The stochastic simulation of biological systems is an increasingly popular technique in Bioinformatics. It is often an enlightening technique, especially for multi-stable systems which dynamics can be hardly captured with ordinary differential equations. To be effective, stochastic simulations should be supported by powerful statistical analysis tools. The simulation-analysis workflow may however result in being computationally expensive, thus compromising the interactivity required in model tuning. In this work we advocate the high-level design of simulators for stochastic systems as a vehicle for building efficient and portable parallel simulators. In particular, the Calculus of Wrapped Components (CWC) simulator, which is designed according to the FastFlow's pattern-based approach, is presented and discussed in this work. FastFlow has been extended to support also clusters of multi-cores with minimal coding effort, assessing the portability of the approach.},
	address = {Belfast, Nothern Ireland, U.K.},
	author = {Marco Aldinucci and Fabio Tordini and Maurizio Drocco and Massimo Torquati and Mario Coppo},
	booktitle = {Proc. of 21st Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-added = {2012-01-20 19:22:15 +0100},
	date-modified = {2017-12-12 13:53:10 +0000},
	doi = {10.1109/PDP.2013.66},
	keywords = {fastflow, bioinformatics},
	month = feb,
	publisher = {IEEE},
	title = {Parallel stochastic simulators in system biology: the evolution of the species},
	url = {http://calvados.di.unipi.it/storage/paper_files/2013_cwc_d_PDP.pdf},
	year = {2013},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2013_cwc_d_PDP.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/PDP.2013.66}}

@incollection{paraphrase:fmco:11,
	abstract = {This paper describes the ParaPhrase project, a new 3-year targeted research project funded under EU Framework 7 Objective 3.4 (Computer Systems), starting in October 2011. ParaPhrase aims to follow a new approach to introducing parallelism using advanced refactoring techniques coupled with high-level parallel design patterns. The refactoring approach will use these design patterns to restructure programs defined as networks of software components into other forms that are more suited to parallel execution. The programmer will be aided by high-level cost information that will be integrated into the refactoring tools. The implementation of these patterns will then use a well-understood algorithmic skeleton approach to achieve good parallelism. A key ParaPhrase design goal is that parallel components are intended to match heterogeneous architectures, defined in terms of CPU/GPU combinations, for example. In order to achieve this, the ParaPhrase approach will map components at link time to the available hardware, and will then re-map them during program execution, taking account of multiple applications, changes in hardware resource availability, the desire to reduce communication costs etc. In this way, we aim to develop a new approach to programming that will be able to produce software that can adapt to dynamic changes in the system environment. Moreover, by using a strong component basis for parallelism, we can achieve potentially significant gains in terms of reducing sharing at a high level of abstraction, and so in reducing or even eliminating the costs that are usually associated with cache management, locking, and synchronisation.},
	author = {Hammond, Kevin and Aldinucci, Marco and Brown, Chris and Cesarini, Francesco and Danelutto, Marco and Gonz{\'a}lez-V{\'e}lez, Horacio and Kilpatrick, Peter and Keller, Rainer and Rossbory, Michael and Shainer, Gilad},
	booktitle = {Formal Methods for Components and Objects: Intl. Symposium, FMCO 2011, Torino, Italy, October 3-5, 2011, Revised Invited Lectures},
	date-added = {2012-06-04 19:21:18 +0200},
	date-modified = {2013-11-24 00:33:27 +0000},
	doi = {10.1007/978-3-642-35887-6_12},
	editor = {Bernhard Beckert and Ferruccio Damiani and Frank S. de Boer and Marcello M. Bonsangue},
	isbn = {978-3-642-35886-9},
	keywords = {paraphrase},
	pages = {218--236},
	publisher = {Springer},
	series = {LNCS},
	title = {The ParaPhrase Project: Parallel Patterns for Adaptive Heterogeneous Multicore Systems},
	url = {http://calvados.di.unipi.it/storage/paper_files/2013_fmco11_paraphrase.pdf},
	volume = {7542},
	year = {2013},
	bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-35887-6_12},
	bdsk-url-2 = {http://calvados.di.unipi.it/storage/paper_files/2013_fmco11_paraphrase.pdf}}

@incollection{toolchain:fmco:11,
	abstract = {Multi-core and many-core platforms are becoming increasingly heterogeneous and asymmetric. This significantly increases the porting and tuning effort required for parallel codes, which in turn often leads to a growing gap between peak machine power and actual application performance. In this work a first step toward the automated optimization of high level skeleton-based parallel code is discussed. The paper presents an abstract annotation model for skeleton programs aimed at formally describing suitable mapping of parallel activities on a high-level platform representation. The derived mapping and scheduling strategies are used to generate optimized run-time code.},
	author = {Marco Aldinucci and Sonia Campa and Fabio Tordini and Massimo Torquati and Peter Kilpatrick},
	booktitle = {Formal Methods for Components and Objects: Intl. Symposium, FMCO 2011, Torino, Italy, October 3-5, 2011, Revised Invited Lectures},
	date-added = {2012-06-04 19:23:25 +0200},
	date-modified = {2013-11-24 00:33:41 +0000},
	doi = {10.1007/978-3-642-35887-6_14},
	editor = {Bernhard Beckert and Ferruccio Damiani and Frank S. de Boer and Marcello M. Bonsangue},
	isbn = {978-3-642-35886-9},
	keywords = {fastflow, paraphrase},
	pages = {257--276},
	publisher = {Springer},
	series = {LNCS},
	title = {An abstract annotation model for skeletons},
	url = {http://calvados.di.unipi.it/storage/paper_files/2013_fmco11_annotation.pdf},
	volume = {7542},
	year = {2013},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2013_fmco11_annotation.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-35887-6_14}}

@inproceedings{DBLP:conf/forte/BonoPT13,
  author       = {Viviana Bono and
                  Luca Padovani and
                  Andrea Tosatto},
  editor       = {Dirk Beyer and
                  Michele Boreale},
  title        = {Polymorphic Types for Leak Detection in a Session-Oriented Functional
                  Language},
  booktitle    = {Formal Techniques for Distributed Systems - Joint {IFIP} {WG} 6.1
                  International Conference, {FMOODS/FORTE} 2013, Held as Part of the
                  8th International Federated Conference on Distributed Computing Techniques,
                  DisCoTec 2013, Florence, Italy, June 3-5, 2013. Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {7892},
  pages        = {83--98},
  publisher    = {Springer},
  year         = {2013},
  url          = {https://doi.org/10.1007/978-3-642-38592-6\_7},
  doi          = {10.1007/978-3-642-38592-6\_7},
  timestamp    = {Sat, 19 Oct 2019 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/forte/BonoPT13.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/fopara/CanaveseCOPR13,
  author       = {Daniele Canavese and
                  Emanuele Cesena and
                  Rachid Ouchary and
                  Marco Pedicini and
                  Luca Roversi},
  editor       = {Ugo Dal Lago and
                  Ricardo Pe{\~{n}}a},
  title        = {Can a Light Typing Discipline Be Compatible with an Efficient Implementation
                  of Finite Fields Inversion?},
  booktitle    = {Foundational and Practical Aspects of Resource Analysis - Third International
                  Workshop, {FOPARA} 2013, Bertinoro, Italy, August 29-31, 2013, Revised
                  Selected Papers},
  series       = {Lecture Notes in Computer Science},
  volume       = {8552},
  pages        = {38--57},
  publisher    = {Springer},
  year         = {2013},
  url          = {https://doi.org/10.1007/978-3-319-12466-7\_3},
  doi          = {10.1007/978-3-319-12466-7\_3},
  timestamp    = {Thu, 23 Jun 2022 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/fopara/CanaveseCOPR13.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tcs/CoppoDDGSST12,
	abstract = {The modelling and analysis of biological systems has deep roots in Mathematics, specifically in the field of Ordinary Differential Equations (ODEs). Alternative approaches based on formal calculi, often derived from process algebras or term rewriting systems, provide a quite complementary way to analyse the behaviour of biological systems. These calculi allow to cope in a natural way with notions like compartments and membranes, which are not easy (sometimes impossible) to handle with purely numerical approaches, and are often based on stochastic simulation methods. Recently, it has also become evident that stochastic effects in regulatory networks play a crucial role in the analysis of such systems. Actually, in many situations it is necessary to use stochastic models. For example when the system to be described is based on the interaction of few molecules, when we are at the presence of a chemical instability, or when we want to simulate the functioning of a pool of entities whose compartmentalised structure evolves dynamically. In contrast, stable metabolic networks, involving a large number of reagents, for which the computational cost of a stochastic simulation becomes an insurmountable obstacle, are efficiently modelled with ODEs. In this paper we define a hybrid simulation method, combining the stochastic approach with ODEs, for systems described in the Calculus of Wrapped Compartments (CWC), a calculus on which we can express the compartmentalisation of a biological system whose evolution is defined by a set of rewrite rules.},
	author = {Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Elena Grassi and Eva Sciacca and Salvatore Spinella and Angelo Troina},
	bibsource = {DBLP, http://dblp.uni-trier.de},
	date-added = {2013-12-12 22:28:07 +0000},
	date-modified = {2024-04-01 11:32:04 +0200},
	doi = {10.1016/j.tcs.2011.12.063},
	ee = {http://dx.doi.org/10.1016/j.tcs.2011.12.063},
	journal = {Theoretical Computer Science},
	keywords = {semantics},
	pages = {75--95},
	title = {Simulation techniques for the calculus of wrapped compartments},
	volume = {431},
	year = {2012},
	bdsk-url-1 = {http://dx.doi.org/10.1016/j.tcs.2011.12.063}}

@inproceedings{artemis:toolchain:12,
	address = {Nuremberg, Germany},
	author = {Torquati, Massimo and Vanneschi, Marco and Amini, Mehdi and Guelton, Serge and Keryell, Ronan and Lanore, Vincent and Pasquier, Fran{\c{c}}ois-Xavier and Barreteau, Michel and Barr{\`e}re, R{\'e}mi and Petrisor, Claudia-Teodora and Lenormand, Eric and Cantini, C. and De Stefani, F.},
	booktitle = {Embedded World Conference},
	date-added = {2012-04-04 12:18:38 +0000},
	date-modified = {2012-04-04 12:21:40 +0000},
	month = feb,
	title = {An innovative compilation tool-chain for embedded multi-core architectures},
	year = {2012}}

@inproceedings{cwcsim:onlinestats:ff:hibb:11,
	abstract = {This work concerns a general technique to enrich parallel version of stochastic simulators for biological systems with tools for on-line statistical analysis of the results. In particular, within the FastFlow parallel programming framework, we describe the methodology and the implementation of a parallel Monte Carlo simulation infrastructure extended with user-defined on-line data filtering and mining functions. The simulator and the on-line analysis were validated on large multi-core platforms and representative proof-of-concept biological systems.},
	address = {Bordeaux, France},
	author = {Marco Aldinucci and Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Eva Sciacca and Salvatore Spinella and Massimo Torquati and Angelo Troina},
	booktitle = {Proc. of Euro-Par Workshops: 2nd Workshop on High Performance Bioinformatics and Biomedicine (HiBB)},
	date-added = {2010-08-15 00:50:09 +0200},
	date-modified = {2017-12-12 14:47:15 +0000},
	doi = {10.1007/978-3-642-29740-3_2},
	editor = {Michael Alexander and Pasqua D'Ambra and Adam Belloum and George Bosilca and Mario Cannataro and Marco Danelutto and Beniamino Di Martino and Michael Gerndt and Emmanuel Jeannot and Raymond Namyst and Jean Roman and Stephen L. Scott and Jesper Larsson Tr{\"a}ff and Geoffroy Vall{\'e}e and Josef Weidendorfer},
	keywords = {bioinformatics, fastflow},
	pages = {3--12},
	publisher = {Springer},
	series = {LNCS},
	title = {On Parallelizing On-Line Statistics for Stochastic Biological Simulations},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_onlinestat_HiBB2011.pdf},
	volume = {7156},
	year = {2012},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2012_onlinestat_HiBB2011.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-29740-3_2}}

@inproceedings{dataflow:pdp:12,
	abstract = {Data flow techniques have been around since the early '70s when they were used in compilers for sequential languages. Shortly after their introduction they were also considered as a possible model for parallel computing, although the impact here was limited. Recently, however, data flow has been identified as a candidate for efficient implementation of various programming models on multi-core architectures. In most cases, however, the burden of determining data flow ``macro'' instructions is left to the programmer, while the compiler/run time system manages only the efficient scheduling of these instructions. We discuss a structured parallel programming approach supporting automatic compilation of programs to macro data flow and we show experimental results demonstrating the feasibility of the approach and the efficiency of the resulting ``object'' code on different classes of state-of-the-art multi-core architectures. The experimental results use different base mechanisms to implement the macro data flow run time support, from plain pthreads with condition variables to more modern and effective lock- and fence-free parallel frameworks. Experimental results comparing efficiency of the proposed approach with those achieved using other, more classical, parallel frameworks are also presented.},
	address = {Garching, Germany},
	author = {Marco Aldinucci and Marco Danelutto and Lorenzo Anardu and Massimo Torquati and Peter Kilpatrick},
	booktitle = {Proc. of Intl. Euromicro PDP 2012: Parallel Distributed and network-based Processing},
	date-added = {2012-10-24 17:29:14 +0000},
	date-modified = {2013-11-24 00:35:34 +0000},
	doi = {10.1109/PDP.2012.44},
	keywords = {fastflow},
	month = feb,
	pages = {27--36},
	publisher = {IEEE},
	title = {Parallel patterns + Macro Data Flow for multi-core programming},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_mdf_PDP.pdf},
	year = {2012},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2012_mdf_PDP.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/PDP.2012.44}}

@inproceedings{denoiser:ff:ipta:12,
	abstract = {In this paper a two-phase filter for removing ``salt and pepper'' noise is proposed. In the first phase, an adaptive median filter is used to identify the set of the noisy pixels; in the second phase, these pixels are restored according to a regularization method, which contains a data-fidelity term reflecting the impulse noise characteristics. The algorithm, which exhibits good performance both in denoising and in restoration, can be easily and effectively parallelized to exploit the full power of multi-core CPUs and GPGPUs; the proposed implementation based on the FastFlow library achieves both close-to-ideal speedup and very good wall-clock execution figures.},
	address = {Istambul, Turkey},
	author = {Marco Aldinucci and Concetto Spampinato and Maurizio Drocco and Massimo Torquati and Simone Palazzo},
	booktitle = {Proc. of 2nd Intl. Conference on Image Processing Theory Tools and Applications (IPTA)},
	date-added = {2012-06-04 18:38:01 +0200},
	date-modified = {2015-09-27 12:53:53 +0000},
	doi = {10.1109/IPTA.2012.6469567},
	editor = {K. Djemal and M. Deriche and W. Puech and Osman N. Ucan},
	isbn = {978-1-4673-2582-0},
	keywords = {fastflow, impact},
	month = oct,
	pages = {97--102},
	publisher = {IEEE},
	title = {A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_2phasedenoiser_ff_ipta.pdf},
	year = {2012},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2012_2phasedenoiser_ff_ipta.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/IPTA.2012.6469567}}

@techreport{fastflow_tutorial:TR-12-04:12,
	author = {Marco Aldinucci and Marco Danelutto and Massimo Torquati},
	date-added = {2011-03-17 23:19:05 +0100},
	date-modified = {2013-11-24 00:34:55 +0000},
	institution = {Universit\`a di Pisa, Dipartimento di Informatica, Italy},
	keywords = {fastflow},
	month = mar,
	number = {TR-12-04},
	title = {FastFlow tutorial},
	url = {http://compass2.di.unipi.it/TR/Files/TR-12-04.pdf.gz},
	year = {2012},
	bdsk-url-1 = {http://compass2.di.unipi.it/TR/Files/TR-12-04.pdf.gz}}

@inproceedings{ff:acaces:12,
	abstract = {Modern computers are built upon multi-core architectures. Achieving peak performance on these architectures is hard and may require a substantial programming effort. The synchronisation of many processes racing to access a common resource (the shared memory) has been a fundamental problem on parallel computing for years, and many solutions have been proposed to address this issue. Non-blocking synchronisation and transactional primitives have been envisioned as a way to reduce memory wall problem. Despite sometimes effective (and exhibiting a great momentum in the research community), they are only one facet of the problem, as their exploitation still requires non-trivial programming skills. With non-blocking philosophy in mind, we propose high-level programming patterns that will relieve the programmer from worrying about low-level details such as synchronisation of racing processes as well as those fine tunings needed to improve the overall performance, like proper (distributed) dynamic memory allocation and effective exploitation of the memory hierarchy.},
	address = {Fiuggi, Italy},
	author = {Fabio Tordini and Marco Aldinucci and Massimo Torquati},
	booktitle = {Advanced Computer Architecture and Compilation for High-Performance and Embedded Systems (ACACES) -- Poster Abstracts},
	date-added = {2012-07-17 17:58:06 +0200},
	date-modified = {2013-11-24 00:36:10 +0000},
	isbn = {9789038219875},
	keywords = {fastflow},
	publisher = {HiPEAC},
	title = {High-level lock-less programming for multicore},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_ACACES_ex-abstract.pdf},
	year = {2012},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2012_ACACES_ex-abstract.pdf}}

@inproceedings{ff:spsc:europar:12,
	abstract = {The use of efficient synchronization mechanisms is crucial for implementing fine grained parallel programs on modern shared cache multi-core architectures. In this paper we study this problem by considering Single-Producer/Single-Consumer (SPSC) coordination using unbounded queues. A novel unbounded SPSC algorithm capable of reducing the row synchronization latency and speeding up Producer-Consumer coordination is presented. The algorithm has been extensively tested on a shared-cache multi-core platform and a sketch proof of correctness is presented. The queues proposed have been used as basic building blocks to implement the FastFlow parallel framework, which has been demonstrated to offer very good performance for fine-grain parallel applications.},
	address = {Rhodes Island, Greece},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Massimiliano Meneghin and Massimo Torquati},
	booktitle = {Proc. of 18th Intl. Euro-Par 2012 Parallel Processing},
	date-added = {2011-04-19 10:22:00 +0200},
	date-modified = {2015-09-27 12:55:20 +0000},
	doi = {10.1007/978-3-642-32820-6_65},
	keywords = {fastflow, paraphrase},
	month = aug,
	pages = {662--673},
	publisher = {Springer},
	series = {LNCS},
	title = {An Efficient Unbounded Lock-Free Queue for Multi-core Systems},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_spsc_europar.pdf},
	volume = {7484},
	year = {2012},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2012_spsc_europar.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-32820-6_65}}

@article{ibm:ijaacs:12,
	abstract = {Today's business applications are increasingly process driven, meaning that the main application logic is executed by a dedicate process engine. In addition, component-oriented software development has been attracting attention for building complex distributed applications. In this paper we present the experiences gained from building a process-driven biometric identification application that makes use of Grid infrastructures via the Grid Component Model (GCM). GCM, besides guaranteeing access to Grid resources, supports autonomic management of notable parallel composite components. This feature is exploited within our biometric identification application to ensure real time identification of fingerprints. Therefore, we briefly introduce the GCM framework and the process engine used, and we describe the implementation of the application by means of autonomic GCM components. Finally, we summarize the results, experiences, and lessons learned focusing on the integration of autonomic GCM components and the process-driven approach.},
	author = {Thomas Weigold and Marco Aldinucci and Marco Danelutto and Vladimir Getov},
	date-added = {2009-08-01 21:01:36 +0200},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1504/IJAACS.2012.047659},
	issn = {1754-8632},
	journal = {Int. J. of Autonomous and Adaptive Communications Systems},
	keywords = {grid},
	number = {3},
	pages = {274--291},
	publisher = {Inderscience Enterprises Ltd.},
	title = {Process-Driven Biometric Identification by means of Autonomic Grid Components},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_JAACS_Weigold.pdf},
	volume = {5},
	year = {2012},
	bdsk-url-1 = {http://www.inderscience.com/info/inarticletoc.php?jcode=ijaacs&year=2012&vol=5&issue=3},
	bdsk-url-2 = {http://calvados.di.unipi.it/storage/paper_files/2012_JAACS_Weigold.pdf},
	bdsk-url-3 = {http://dx.doi.org/10.1504/IJAACS.2012.047659}}

@article{mdf:hplgpu:ppl:12,
	abstract = {We propose a data flow based run time system as an efficient tool for supporting execution of parallel code on heterogeneous architectures hosting both multicore CPUs and GPUs. We discuss how the proposed run time system may be the target of both structured parallel applications developed using algorithmic skeletons/parallel design patterns and also more ``domain specific'' programming models. Experimental results demonstrating the feasibility of the approach are presented.},
	annote = {Extended version of Intl. Workshop on High-level Programming for Heterogeneous and Hierarchical Parallel Systems (HLPGPU)},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Massimo Torquati},
	date-added = {2012-04-25 13:20:40 +0000},
	date-modified = {2015-09-27 12:55:11 +0000},
	doi = {10.1142/S0129626412400063},
	issn = {0129-6264},
	journal = {Parallel Processing Letters},
	keywords = {fastflow, paraphrase},
	month = jun,
	number = {2},
	title = {Targeting heterogeneous architectures via macro data flow},
	url = {http://calvados.di.unipi.it/storage/paper_files/2012_mdf_PPL-hplgpu.pdf},
	volume = {22},
	year = {2012},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2012_mdf_PPL-hplgpu.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1142/S0129626412400063}}

@inproceedings{DBLP:conf/pdp/SpigaG12,
	author = {Filippo Spiga and Ivan Girotto},
	bibsource = {dblp computer science bibliography, http://dblp.org},
	biburl = {http://dblp.org/rec/bib/conf/pdp/SpigaG12},
	booktitle = {Proceedings of the 20th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, {PDP} 2012, Munich, Germany, February 15-17, 2012},
	doi = {10.1109/PDP.2012.72},
	optcrossref = {DBLP:conf/pdp/2012},
	pages = {368--375},
	timestamp = {Thu, 25 May 2017 00:43:12 +0200},
	title = {phiGEMM: {A} {CPU-GPU} Library for Porting Quantum {ESPRESSO} on Hybrid Systems},
	url = {https://doi.org/10.1109/PDP.2012.72},
	year = {2012},
	bdsk-url-1 = {https://doi.org/10.1109/PDP.2012.72}}

@article{DBLP:journals/corr/abs-1202-2086,
  author       = {Viviana Bono and
                  Luca Padovani},
  title        = {Typing Copyless Message Passing},
  journal      = {Log. Methods Comput. Sci.},
  volume       = {8},
  number       = {1},
  year         = {2012},
  url          = {https://doi.org/10.2168/LMCS-8(1:17)2012},
  doi          = {10.2168/LMCS-8(1:17)2012},
  timestamp    = {Thu, 25 Jun 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1202-2086.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/ecoop/BonoKM12,
  author       = {Viviana Bono and
                  Jarek Kusmierek and
                  Mauro Mulatero},
  editor       = {James Noble},
  title        = {Magda: {A} New Language for Modularity},
  booktitle    = {{ECOOP} 2012 - Object-Oriented Programming - 26th European Conference,
                  Beijing, China, June 11-16, 2012. Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {7313},
  pages        = {560--588},
  publisher    = {Springer},
  year         = {2012},
  url          = {https://doi.org/10.1007/978-3-642-31057-7\_25},
  doi          = {10.1007/978-3-642-31057-7\_25},
  timestamp    = {Fri, 27 Mar 2020 08:51:54 +0100},
  biburl       = {https://dblp.org/rec/conf/ecoop/BonoKM12.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1206-5112,
  author       = {Viviana Bono and
                  Marcin Benke and
                  Aleksy Schubert},
  title        = {Lucretia - a type system for objects in languages with reflection},
  journal      = {CoRR},
  volume       = {abs/1206.5112},
  year         = {2012},
  url          = {http://arxiv.org/abs/1206.5112},
  eprinttype   = {arXiv},
  eprint       = {1206.5112},
  timestamp    = {Mon, 13 Aug 2018 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1206-5112.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/fuin/PimentelRR12,
  author       = {Elaine Pimentel and
                  Simona Ronchi Della Rocca and
                  Luca Roversi},
  title        = {Intersection Types from a Proof-theoretic Perspective},
  journal      = {Fundam. Informaticae},
  volume       = {121},
  number       = {1-4},
  pages        = {253--274},
  year         = {2012},
  url          = {https://doi.org/10.3233/FI-2012-778},
  doi          = {10.3233/FI-2012-778},
  timestamp    = {Fri, 18 Sep 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/fuin/PimentelRR12.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1212-4483,
  author       = {Luca Roversi},
  title        = {Extending a system in the calculus of structures with a self-dual
                  quantifier},
  journal      = {CoRR},
  volume       = {abs/1212.4483},
  year         = {2012},
  url          = {http://arxiv.org/abs/1212.4483},
  eprinttype   = {arXiv},
  eprint       = {1212.4483},
  timestamp    = {Mon, 13 Aug 2018 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1212-4483.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1212-4669,
  author       = {Luca Roversi},
  title        = {Communication, and concurrency with logic-based restriction inside
                  a calculus of structures},
  journal      = {CoRR},
  volume       = {abs/1212.4669},
  year         = {2012},
  url          = {http://arxiv.org/abs/1212.4669},
  eprinttype   = {arXiv},
  eprint       = {1212.4669},
  timestamp    = {Mon, 13 Aug 2018 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1212-4669.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tcsb/Coppo/DDGGT11,
	abstract = {The Stochastic Calculus of Wrapped Compartments (SCWC) is a recently proposed variant of the Stochastic Calculus of Looping Sequences (SCLS), a language for the representation and simulation of biological systems. In this work we apply SCWC to model a newly discovered ammonium transporter. This transporter is believed to play a fundamental role for plant mineral acquisition, which takes place in the arbuscular mycorrhiza, the most wide-spread plant-fungus symbiosis on earth. Investigating this kind of symbiosis is considered one of the most promising ways to develop methods to nurture plants in more natural manners, avoiding the complex chemical productions used nowadays to produce artificial fertilizers. In our experiments the passage of NH3/NH4+ from the fungus to the plant has been dissected in known and hypothetical mechanisms; with the model so far we have been able to simulate the behavior of the system under different conditions.  Our simulations confirmed some of the latest experimental results about the LjAMT2;2 transporter. Moreover, by comparing the behaviour of LjAMT2;2 with the behaviour of another ammonium transporter which exists in plants, viz. LjAMT1;1, our simulations support an hypothesis about why LjAMT2;2 is so selectively expressed in arbusculated cells.},
	author = {Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Elena Grassi and Mike Guether and Angelo Troina},
	date-added = {2013-12-12 22:25:24 +0000},
	date-modified = {2024-04-01 11:32:12 +0200},
	doi = {10.1007/978-3-642-19748-2_5},
	journal = {Transactions on Computational Systems Biology},
	keywords = {semantics},
	number = {13},
	pages = {85--109},
	title = {Modelling Ammonium Transporters in Arbuscular Mycorrhiza Symbiosis},
	volume = {6575},
	year = {2011},
	bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-19748-2_5}}

@inproceedings{DBLP:journals/corr/abs-1109-1363,
	abstract = {Arbuscular mycorrhiza (AM) is the most wide-spread plant-fungus symbiosis on earth. Investigating this kind of symbiosis is considered one of the most promising ways to develop methods to nurture plants in more natural manners, avoiding the complex chemical productions used nowadays to produce artificial fertilizers. In previous work we used the Calculus of Wrapped Compartments (CWC) to investigate different phases of the AM symbiosis. In this paper, we continue this line of research by modelling the colonisation of the plant root cells by the fungal hyphae spreading in the soil. This study requires the description of some spatial interaction. Although CWC has no explicit feature modelling a spatial geometry, the compartment labelling feature can be effectively exploited to define a discrete surface topology outlining the relevant sectors which determine the spatial properties of the system under consideration. Different situations and interesting spatial properties can be modelled and analysed in such a lightweight framework (which has not an explicit notion of geometry with coordinates and spatial metrics), thus exploiting the existing CWC simulation tool.},
	address = {Aachen, Germany},
	author = {Cristina Calcagno and Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Eva Sciacca and Salvatore Spinella and Angelo Troina},
	bibsource = {DBLP, http://dblp.uni-trier.de},
	booktitle = {Proc. of 3rd Intl. Workshop on Computational Models for Cell Processes (CompMod)},
	date-added = {2013-12-12 22:25:03 +0000},
	date-modified = {2017-12-12 13:51:04 +0000},
	editor = {Ion Petre and Erik P. de Vink},
	ee = {http://dx.doi.org/10.4204/EPTCS.67.3},
	month = sep,
	pages = {3--18},
	series = {EPTCS},
	title = {Modelling Spatial Interactions in the Arbuscular Mycorrhizal Symbiosis using the Calculus of Wrapped Compartments},
	volume = {67},
	year = {2011}}

@inproceedings{ff:acc:europar:11,
	abstract = {FastFlow is a programming framework specifically targeting cache-coherent shared-memory multicores. It is implemented as a stack of C++ template libraries built on top of lock-free (and memory fence free) synchronization mechanisms. Its philosophy is to combine programmability with performance. In this paper a new FastFlow programming methodology aimed at supporting parallelization of existing sequential code via offloading onto a dynamically created software accelerator is presented. The new methodology has been validated using a set of simple micro-benchmarks and some real applications.},
	address = {Bordeaux, France},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Massimiliano Meneghin and Massimo Torquati},
	booktitle = {Proc. of 17th Intl. Euro-Par 2011 Parallel Processing},
	date-added = {2012-06-04 18:35:57 +0200},
	date-modified = {2013-12-12 00:46:59 +0000},
	doi = {10.1007/978-3-642-23397-5_17},
	editor = {E. Jeannot and R. Namyst and J. Roman},
	keywords = {fastflow},
	month = aug,
	pages = {170--181},
	publisher = {Springer},
	series = {LNCS},
	title = {Accelerating code on multi-cores with FastFlow},
	url = {http://calvados.di.unipi.it/storage/paper_files/2011_fastflow_acc_europar.pdf},
	volume = {6853},
	year = {2011},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2011_fastflow_acc_europar.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-23397-5_17}}

@inproceedings{ff:cwc:pdp:11,
	abstract = {The stochastic simulation of biological systems is an increasingly popular technique in bioinformatics. It often is an enlightening technique, which may however result in being computational expensive. We discuss the main opportunities to speed it up on multi-core platforms, which pose new challenges for parallelisation techniques. These opportunities are developed in two general families of solutions involving both the single simulation and a bulk of independent simulations (either replicas of derived from parameter sweep). Proposed solutions are tested on the parallelisation of the CWC simulator (Calculus of Wrapped Compartments) that is carried out according to proposed solutions by way of the FastFlow programming framework making possible fast development and efficient execution on multi-cores.},
	address = {Ayia Napa, Cyprus},
	author = {Marco Aldinucci and Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Massimo Torquati and Angelo Troina},
	booktitle = {Proc. of 19th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP)},
	date-added = {2012-02-25 01:21:25 +0000},
	date-modified = {2017-12-12 13:51:21 +0000},
	doi = {10.1109/PDP.2011.81},
	editor = {Yiannis Cotronis and Marco Danelutto and George Angelos Papadopoulos},
	keywords = {fastflow},
	month = feb,
	pages = {318--325},
	publisher = {IEEE},
	title = {On Designing Multicore-Aware Simulators for Biological Systems},
	url = {http://calvados.di.unipi.it/storage/paper_files/2011_ff_cwc_sim_PDP.pdf},
	year = {2011},
	bdsk-url-1 = {http://arxiv.org/pdf/1010.2438v2},
	bdsk-url-2 = {http://calvados.di.unipi.it/storage/paper_files/2011_ff_cwc_sim_PDP.pdf},
	bdsk-url-3 = {http://dx.doi.org/10.1109/PDP.2011.81}}

@inproceedings{libero:cgsymph:10,
	abstract = {We describe a lightweight prototype framework (LIBERO) designed for experimentation with behavioural skeletons-components implementing a well-known parallelism exploitation pattern and a rule-based autonomic manager taking care of some non-functional feature related to pattern computation. LIBERO supports multiple autonomic managers within the same behavioural skeleton, each taking care of a different non-functional concern. We introduce LIBERO -- built on plain Java and JBoss -- and discuss how multiple managers may be coordinated to achieve a common goal using a two-phase coordination protocol developed in earlier work. We present experimental results that demonstrate how the prototype may be used to investigate autonomic management of multiple, independent concerns.},
	address = {Ischia, Italy},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Vamir Xhagjika},
	booktitle = {Euro-Par 2010 Workshops, Proc. of the CoreGrid Workshop on Grids, Clouds and P2P Computing},
	date-added = {2011-09-12 14:58:27 +0200},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1007/978-3-642-21878-1_30},
	keywords = {grid},
	month = sep,
	pages = {237--245},
	publisher = {Springer},
	series = {LNCS},
	title = {LIBERO: a framework for autonomic management of multiple non-functional concerns},
	url = {http://calvados.di.unipi.it/storage/paper_files/2011_libero_coregridworkshop2010.pdf},
	volume = {6586},
	year = {2011},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2011_libero_coregridworkshop2010.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-21878-1_30}}

@inproceedings{stochkit-ff:hibb:10,
	abstract = {The stochastic modelling of biological systems is an informative, and in some cases, very adequate technique, which may however result in being more expensive than other modelling approaches, such as differential equations. We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations. StochKit-FF is based on the FastFlow programming toolkit for multicores and exploits the novel concept of selective memory. We experiment StochKit-FF on a model of HIV infection dynamics, with the aim of extracting information from efficiently run experiments, here in terms of average and variance and, on a longer term, of more structured data.},
	address = {Ischia, Italy},
	author = {Marco Aldinucci and Andrea Bracciali and Pietro Li{\`o} and Anil Sorathiya and Massimo Torquati},
	booktitle = {Euro-Par 2010 Workshops, Proc. of the 1st Workshop on High Performance Bioinformatics and Biomedicine (HiBB)},
	date-added = {2012-04-12 11:23:46 +0000},
	date-modified = {2013-11-24 00:36:38 +0000},
	doi = {10.1007/978-3-642-21878-1_21},
	keywords = {bioinformatics},
	month = aug,
	pages = {167--175},
	publisher = {Springer},
	series = {{LNCS}},
	title = {{StochKit-FF}: Efficient Systems Biology on Multicore Architectures},
	url = {http://calvados.di.unipi.it/storage/paper_files/2010_stochkit-ff_hibb.pdf},
	volume = {6586},
	year = {2011},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2010_stochkit-ff_hibb.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-21878-1_21}}

@techreport{TR-11-06,
	author = {Marco Aldinucci and Salvatore Ruggieri and Massimo Torquati},
	date-added = {2012-04-15 18:40:07 +0000},
	date-modified = {2013-11-24 00:37:04 +0000},
	institution = {Universit\`a di Pisa, Dipartimento di Informatica, Italy},
	keywords = {fastflow},
	month = mar,
	number = {TR-11-06},
	title = {Porting Decision Tree Building and Pruning Algorithms to Multicore using FastFlow},
	url = {http://compass2.di.unipi.it/TR/Files/TR-11-06.pdf.gz},
	year = {2011},
	bdsk-url-1 = {http://compass2.di.unipi.it/TR/Files/TR-11-06.pdf.gz}}

@article{11:cms,
	author = {Grandi, Claudio and Bockelman, Brian and Bonacorsi, Daniele and Fisk, Ian and Gonz{\'a}lez Caballero, Isidro and Farina, Fabio and Hern{\'a}ndez Calama, Jos{\'e} M. and Padhi, Sanjay and Sarkar, Sabyasachi and Sciab{\`a}, Andrea and Sfiligoi, Igor and Spiga, Filippo and {\'U}beda Garc{\'\i}a, Mario and Van Der Ster, Daniel C. and Zvada, Marian},
	booktitle = {Journal of Physics: Conference Series},
	date-modified = {2024-08-18 14:42:47 +0200},
	keywords = {parallel},
	month = dec,
	pages = {062032},
	title = {CMS Distributed Computing Integration in the LHC sustained operations era},
	volume = {331},
	year = {2011}}

@article{DBLP:journals/scp/BettiniBV11,
  author       = {Lorenzo Bettini and
                  Viviana Bono and
                  Betti Venneri},
  title        = {Delegation by object composition},
  journal      = {Sci. Comput. Program.},
  volume       = {76},
  number       = {11},
  pages        = {992--1014},
  year         = {2011},
  url          = {https://doi.org/10.1016/j.scico.2010.04.006},
  doi          = {10.1016/J.SCICO.2010.04.006},
  timestamp    = {Wed, 17 Feb 2021 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/journals/scp/BettiniBV11.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/tgc/BonoCCD11,
  author       = {Viviana Bono and
                  Sara Capecchi and
                  Ilaria Castellani and
                  Mariangiola Dezani{-}Ciancaglini},
  editor       = {Roberto Bruni and
                  Vladimiro Sassone},
  title        = {A Reputation System for Multirole Sessions},
  booktitle    = {Trustworthy Global Computing - 6th International Symposium, {TGC}
                  2011, Aachen, Germany, June 9-10, 2011. Revised Selected Papers},
  series       = {Lecture Notes in Computer Science},
  volume       = {7173},
  pages        = {1--24},
  publisher    = {Springer},
  year         = {2011},
  url          = {https://doi.org/10.1007/978-3-642-30065-3\_1},
  doi          = {10.1007/978-3-642-30065-3\_1},
  timestamp    = {Thu, 26 Sep 2024 16:29:51 +0200},
  biburl       = {https://dblp.org/rec/conf/tgc/BonoCCD11.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:journals/corr/abs-1108-0466,
  author       = {Viviana Bono and
                  Luca Padovani},
  editor       = {Alexandra Silva and
                  Simon Bliudze and
                  Roberto Bruni and
                  Marco Carbone},
  title        = {Polymorphic Endpoint Types for Copyless Message Passing},
  booktitle    = {Proceedings Fourth Interaction and Concurrency Experience, {ICE} 2011,
                  Reykjavik, Iceland, 9th June 2011},
  series       = {{EPTCS}},
  volume       = {59},
  pages        = {52--67},
  year         = {2011},
  url          = {https://doi.org/10.4204/EPTCS.59.5},
  doi          = {10.4204/EPTCS.59.5},
  timestamp    = {Thu, 26 Sep 2024 16:29:51 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1108-0466.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/tlsdkcs/BonfanteCGILLMMRV11,
  author       = {Paola Bonfante and
                  Francesca Cordero and
                  Stefano Ghignone and
                  Dino Ienco and
                  Luisa Lanfranco and
                  Giorgio Leonardi and
                  Rosa Meo and
                  Stefania Montani and
                  Luca Roversi and
                  Alessia Visconti},
  title        = {A Modular Database Architecture Enabled to Comparative Sequence Analysis},
  journal      = {Trans. Large Scale Data Knowl. Centered Syst.},
  volume       = {4},
  pages        = {124--147},
  year         = {2011},
  url          = {https://doi.org/10.1007/978-3-642-23740-9\_6},
  doi          = {10.1007/978-3-642-23740-9\_6},
  timestamp    = {Wed, 20 May 2026 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/tlsdkcs/BonfanteCGILLMMRV11.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/fopara/CesenaPR11,
  author       = {Emanuele Cesena and
                  Marco Pedicini and
                  Luca Roversi},
  editor       = {Ricardo Pe{\~{n}}a and
                  Marko C. J. D. van Eekelen and
                  Olha Shkaravska},
  title        = {Typing a Core Binary-Field Arithmetic in a Light Logic},
  booktitle    = {Foundational and Practical Aspects of Resource Analysis - Second International
                  Workshop, {FOPARA} 2011, Madrid, Spain, May 19, 2011, Revised Selected
                  Papers},
  series       = {Lecture Notes in Computer Science},
  volume       = {7177},
  pages        = {19--35},
  publisher    = {Springer},
  year         = {2011},
  url          = {https://doi.org/10.1007/978-3-642-32495-6\_2},
  doi          = {10.1007/978-3-642-32495-6\_2},
  timestamp    = {Tue, 14 May 2019 10:00:45 +0200},
  biburl       = {https://dblp.org/rec/conf/fopara/CesenaPR11.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/tlca/Roversi11,
  author       = {Luca Roversi},
  editor       = {C.{-}H. Luke Ong},
  title        = {Linear Lambda Calculus and Deep Inference},
  booktitle    = {Typed Lambda Calculi and Applications - 10th International Conference,
                  {TLCA} 2011, Novi Sad, Serbia, June 1-3, 2011. Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {6690},
  pages        = {184--197},
  publisher    = {Springer},
  year         = {2011},
  url          = {https://doi.org/10.1007/978-3-642-21691-6\_16},
  doi          = {10.1007/978-3-642-21691-6\_16},
  timestamp    = {Tue, 14 May 2019 10:00:41 +0200},
  biburl       = {https://dblp.org/rec/conf/tlca/Roversi11.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:journals/corr/abs-1006-5099,
	abstract = {The Calculus of Wrapped Compartments (CWC) is a variant of the Calculus of Looping Sequences (CLS). While keeping the same expressiveness, CWC strongly simplifies the development of automatic tools for the analysis of biological systems. The main simplification consists in the removal of the sequencing operator, thus lightening the formal treatment of the patterns to be matched in a term (whose complexity in CLS is strongly affected by the variables matching in the sequences). We define a stochastic semantics for this new calculus. As an application we model the interaction between macrophages and apoptotic neutrophils and a mechanism of gene regulation in E.Coli.},
	address = {Paphos, Cyprus},
	author = {Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Elena Grassi and Angelo Troina},
	bibsource = {DBLP, http://dblp.uni-trier.de},
	booktitle = {Proc. of 8th Workshop on Quantitative Aspects of Programming Languages (QAPL)},
	date-added = {2013-12-12 22:24:44 +0000},
	date-modified = {2017-12-12 13:49:43 +0000},
	editor = {Alessandra Di Pierro and Gethin Norman},
	ee = {http://dx.doi.org/10.4204/EPTCS.28.6},
	month = mar,
	pages = {82--98},
	series = {EPTCS},
	title = {Stochastic Calculus of Wrapped Compartments},
	volume = {28},
	year = {2010}}

@inproceedings{DBLP:journals/corr/abs-1011-0494,
	abstract = {The modelling and analysis of biological systems has deep roots in Mathematics, specifically in the field of ordinary differential equations (ODEs). Alternative approaches based on formal calculi, often derived from process algebras or term rewriting systems, provide a quite complementary way to analyze the behaviour of biological systems. These calculi allow to cope in a natural way with notions like compartments and membranes, which are not easy (sometimes impossible) to handle with purely numerical approaches, and are often based on stochastic simulation methods. Recently, it has also become evident that stochastic effects in regulatory networks play a crucial role in the analysis of such systems. Actually, in many situations it is necessary to use stochastic models. For example when the system to be described is based on the interaction of few molecules, when we are at the presence of a chemical instability, or when we want to simulate the functioning of a pool of entities whose compartmentalised structure evolves dynamically. In contrast, stable metabolic networks, involving a large number of reagents, for which the computational cost of a stochastic simulation becomes an insurmountable obstacle, are efficiently modelled with ODEs. In this paper we define a hybrid simulation method, combining the stochastic approach with ODEs, for systems described in CWC, a calculus on which we can express the compartmentalisation of a biological system whose evolution is defined by a set of rewrite rules.},
	address = {Jena, Germany},
	author = {Mario Coppo and Ferruccio Damiani and Maurizio Drocco and Elena Grassi and Eva Sciacca and Salvatore Spinella and Angelo Troina},
	bibsource = {DBLP, http://dblp.uni-trier.de},
	booktitle = {Proc. of 4th Workshop on Membrane Computing and Biologically Inspired Process Calculi (MeCBIC)},
	date-added = {2013-12-12 22:24:23 +0000},
	date-modified = {2025-05-24 09:24:27 +0200},
	editor = {Gabriel Ciobanu and Maciej Koutny},
	ee = {http://dx.doi.org/10.4204/EPTCS.40.8},
	keywords = {semantics},
	month = aug,
	pages = {102--120},
	series = {EPTCS},
	title = {Hybrid Calculus of Wrapped Compartments},
	volume = {40},
	year = {2010}}

@inproceedings{fastflow:pdp:10,
	abstract = {Shared memory multiprocessors have returned to popularity thanks to rapid spreading of commodity multi-core architectures. However, little attention has been paid to supporting effective streaming applications on these architectures. In this paper we describe FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than them on a given real world application: the speedup of FastFlow over other solutions may be substantial for fine grain tasks, for example +35% over OpenMP, +226% over Cilk, +96% over TBB for the alignment of protein P01111 against UniProt DB using the Smith-Waterman algorithm.},
	address = {Pisa, Italy},
	author = {Marco Aldinucci and Massimiliano Meneghin and Massimo Torquati},
	booktitle = {Proc. of Intl. Euromicro PDP 2010: Parallel Distributed and network-based Processing},
	date-added = {2007-10-26 01:02:32 +0200},
	date-modified = {2013-11-24 00:38:51 +0000},
	doi = {10.1109/PDP.2010.93},
	editor = {Marco Danelutto and Tom Gross and Julien Bourgeois},
	keywords = {fastflow},
	month = feb,
	pages = {195--199},
	publisher = {IEEE},
	title = {Efficient {Smith-Waterman} on multi-core with FastFlow},
	url = {http://calvados.di.unipi.it/storage/paper_files/2010_fastflow_SW_PDP.pdf},
	year = {2010},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2010_fastflow_SW_PDP.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/PDP.2010.93}}

@techreport{fastflow_acc:tr-10-03,
	abstract = {Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and quite hard to optimise; providing multi-core programmers with optimising tools and programming frameworks is a nowadays challenge. Few efforts have been done to support effective streaming applications on these architectures. In this paper we introduce FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than all of them in a set of micro-benchmarks and on a real world application; the speedup edge of FastFlow over other solutions might be bold for fine grain tasks, as an example +35% on OpenMP, +226% on Cilk, +96% on TBB for the alignment of protein P01111 against UniProt DB using Smith-Waterman algorithm.},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Massimiliano Meneghin and Massimo Torquati},
	date-added = {2009-09-08 16:14:34 +0200},
	date-modified = {2013-11-24 00:39:01 +0000},
	institution = {Universit{\`a} di Pisa, Dipartimento di Informatica, Italy},
	keywords = {fastflow},
	month = feb,
	number = {TR-10-03},
	title = {Accelerating sequential programs using {FastFlow} and self-offloading},
	year = {2010},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/TR-09-12.pdf}}

@inproceedings{fastflow_c45:emclpkdd,
	abstract = {The whole computer hardware industry embraced multicores. For these machines, the extreme optimisation of sequential algorithms is no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an approach for easy-yet-efficient porting of an implementation of the C4.5 algorithm on multicores. The parallel porting requires minimal changes to the original sequential code, and it is able to exploit up to 7X speedup on an Intel dual-quad core machine.},
	address = {Barcelona, Spain},
	author = {Marco Aldinucci and Salvatore Ruggieri and Massimo Torquati},
	booktitle = {Proc. of European Conference in Machine Learning and Knowledge Discovery in Databases (ECML PKDD)},
	date-added = {2010-06-15 21:03:56 +0200},
	date-modified = {2013-11-24 00:38:07 +0000},
	doi = {10.1007/978-3-642-15880-3_7},
	editor = {Jos{\'e} L. Balc{\'a}zar and Francesco Bonchi and Aristides Gionis and Mich{\`e}le Sebag},
	keywords = {fastflow},
	month = sep,
	pages = {7--23},
	publisher = {Springer},
	series = {LNCS},
	title = {Porting Decision Tree Algorithms to Multicore using {FastFlow}},
	url = {http://calvados.di.unipi.it/storage/paper_files/2010_c45FF_ECMLPKDD.pdf},
	volume = {6321},
	year = {2010},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2010_c45FF_ECMLPKDD.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-15880-3_7}}

@incollection{ff:hpc-europa:10,
	abstract = {The stochastic simulation of natural systems is a very informative but happens be computationally expensive. We present StochKit-FF, an parallel version of StochKit, a reference toolkit for stochastic simulations that sustantially improves StochKit performances on multi-core platforms.},
	author = {Marco Aldinucci},
	booktitle = {HPC-Europa2: Science and Supercomputing in Europe, research highlights 2010},
	date-added = {2011-06-18 18:43:19 +0200},
	date-modified = {2013-11-24 00:40:04 +0000},
	keywords = {bioinformatics, fastflow},
	publisher = {Cineca},
	title = {Efficient Parallel {MonteCarlo} with {FastFlow}},
	url = {http://calvados.di.unipi.it/storage/paper_files/2010-ff_hpceuropa2_092-inform-Aldinucci.pdf},
	year = {2010},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2010-ff_hpceuropa2_092-inform-Aldinucci.pdf}}

@inproceedings{ibm:autonomics:09,
	abstract = {Today's business applications are increasingly process driven, meaning that the main application logic is executed by a dedicate process engine. In addition, component-oriented software development has been attracting attention for building complex distributed applications. In this paper we present the experiences gained from building a process-driven biometric identification application which makes use of Grid infrastructures via the Grid Component Model (GCM). GCM, besides guaranteeing access to Grid resources, supports autonomic management of notable parallel composite components. This feature is exploited within our biometric identification application to ensure real time identification of fingerprints. Therefore, we briefly introduce the GCM framework and the process engine used, and we describe the implementation of the application using autonomic GCM components. Finally, we summarize the results, experiences, and lessons learned focusing on the integration of autonomic GCM components and the process-driven approach.},
	address = {Limassol, Cyprus},
	annote = {ISBN: 978-3-642-11481-6},
	author = {Thomas Weigold and Marco Aldinucci and Marco Danelutto and Vladimir Getov},
	booktitle = {Autonomic Computing and Communications Systems Third International ICST Conference, Autonomics 2009, Limassol, Cyprus, September 9-11, 2009, Revised Selected Papers},
	date-added = {2010-02-13 16:13:10 +0100},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1007/978-3-642-11482-3_7},
	editor = {Athanasios V. Vasilakos and Roberto Beraldi and Roy Friedman and Marco Mamei},
	issn = {1867-8211},
	keywords = {grid},
	pages = {98--113},
	publisher = {Springer},
	series = {{Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST)}},
	title = {Integrating Autonomic Grid Components and Process-Driven Business Applications},
	url = {http://calvados.di.unipi.it/storage/paper_files/2010_BS_autonomics09.pdf},
	volume = {23},
	year = {2010},
	bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-11482-3_7},
	bdsk-url-2 = {http://calvados.di.unipi.it/storage/paper_files/2010_BS_autonomics09.pdf}}

@inproceedings{multicoreske:parco:09,
	abstract = {We discuss how algorithmic skeletons (and structured parallel programming models in general) can be used to efficiently and seamlessly program multi-core as well as many-core systems. We introduce a new version of the muskel skeleton library that can be used to target multi/many-core systems and we present experimental results that demonstrate the feasibility of the approach. The experimental results presented also give an idea of the computational grains that can be exploited on current, state-of-the-art multi-core systems.},
	address = {Lyon, France},
	annote = {ISBN: 978-1-60750-529-7},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick},
	booktitle = {Parallel Computing: From Multicores and GPU's to Petascale (Proc. of {PARCO 2009}, Lyon, France)},
	date-added = {2009-06-03 17:56:19 +0200},
	date-modified = {2012-11-24 09:43:35 +0000},
	doi = {10.3233/978-1-60750-530-3-265},
	editor = {Barbara Chapman and Fr{\'e}d{\'e}ric Desprez and Gerhard R. Joubert and Alain Lichnewsky and Frans Peters and Thierry Priol},
	pages = {265--272},
	publisher = {IOS press},
	series = {Advances in Parallel Computing},
	title = {Skeletons for multi/many-core systems},
	url = {http://calvados.di.unipi.it/storage/paper_files/2010_muskel_multicore_parco.pdf},
	volume = {19},
	year = {2010},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2010_muskel_multicore_parco.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.3233/978-1-60750-530-3-265}}

@incollection{multiple-nf-concern:cgsymph:09:book,
	abstract = {We introduce and address the problem of concurrent autonomic management of different non-functional concerns in parallel applications build as a hierarchical composition of behavioural skeletons. We first define the problems arising when multiple concerns are dealt with by independent managers, then we propose a methodology supporting coordinated management, and finally we discuss how autonomic management of multiple concerns may be implemented in a typical use case. Being based on the behavioural skeleton concept proposed in the CoreGRID GCM, it is anticipated that the methodology will be readily integrated into the current reference implementation of GCM based on Java ProActive and running on top of major grid middleware systems.},
	annote = {ISBN: 978-1-4419-6793-0(Proc. of the CoreGRID Symposium 2009)},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick},
	booktitle = {Grids, P2P and Services Computing},
	date-added = {2009-06-30 12:24:06 +0200},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1007/978-1-4419-6794-7_8},
	editor = {Fr\'ed\'eric Desprez and Vladimir Getov and Thierry Priol and Ramin Yahyapour},
	keywords = {grid},
	month = aug,
	pages = {89--103},
	publisher = {Springer},
	series = {CoreGRID},
	title = {Autonomic Management of Multiple Non-Functional Concerns in Behavioural Skeletons},
	url = {http://calvados.di.unipi.it/storage/paper_files/2009_CGSymph_Autonomic_BeSke.pdf},
	year = {2010},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2009_CGSymph_Autonomic_BeSke.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-1-4419-6794-7_8}}

@article{stochkitff:ercimnews:10,
	abstract = {The human immune system fights pathogens using an articulated set of strategies whose function is to maintain in health the organism. A large effort to formally model such a complex system using a computational approach is currently underway, with the goal of developing a discipline for engineering "synthetic" immune responses. This requires the integration of a range of analysis techniques developed for formally reasoning about the behaviour of complex dynamical systems. Furthermore, a novel class of software tools has to be developed, capable of efficiently analysing these systems on widely accessible computing platforms, such as commodity multi-core architectures..},
	author = {Marco Aldinucci and Andrea Bracciali and Pietro Li{\`o}},
	date-added = {2010-07-02 20:32:31 +0200},
	date-modified = {2013-11-24 00:38:19 +0000},
	issn = {0926-4981},
	journal = {ERCIM News},
	keywords = {bioinformatics, fastflow},
	month = jul,
	pages = {40--41},
	title = {Formal Synthetic Immunology},
	url = {http://ercim-news.ercim.eu/images/stories/EN82/EN82-web.pdf},
	volume = {82},
	year = {2010},
	bdsk-url-1 = {http://ercim-news.ercim.eu/images/stories/EN82/EN82-web.pdf}}

@inbook{fastflow:parco:09,
	abstract = {Shared-memory multi-core architectures are becoming increasingly popular. While their parallelism and peak performance is ever increasing, their efficiency is often disappointing due to memory fence overheads. In this paper we present FastFlow, a programming methodology based on lock-free queues explicitly designed for programming streaming applications on multi-cores. The potential of FastFlow is evaluated on micro-benchmarks and on the Smith-Waterman sequence alignment application, which exhibits a substantial speedup against the state-of-the-art multi-threaded implementation (SWPS3 x86/SSE2).},
	author = {Aldinucci, Marco and Danelutto, Marco and Meneghin, Massimiliano and Torquati, Massimo and Kilpatrick, Peter},
	date-modified = {2019-03-25 23:17:15 +0100},
	doi = {10.3233/978-1-60750-530-3-273},
	keywords = {fastflow},
	language = {English},
	opteditor = {Barbara Chapman and Fr{\'e}d{\'e}ric Desprez and Gerhard R. Joubert and Alain Lichnewsky and Frans Peters and Thierry Priol},
	pages = {273--280},
	publisher = {Elsevier},
	series = {Advances in Parallel Computing},
	title = {Efficient streaming applications on multi-core with FastFlow: The biosequence alignment test-bed},
	url = {https://iris.unito.it/retrieve/handle/2318/67254/690859/2009_fastflow_parco.pdf},
	volume = {19},
	year = {2010},
	bdsk-url-1 = {https://iris.unito.it/retrieve/handle/2318/67254/690859/2009_fastflow_parco.pdf},
	bdsk-url-2 = {https://doi.org/10.3233/978-1-60750-530-3-273}}

@article{DBLP:journals/fuin/KusmierekB10,
  author       = {Jaroslaw D. M. Kusmierek and
                  Viviana Bono},
  title        = {Big-step Operational Semantics Revisited},
  journal      = {Fundam. Informaticae},
  volume       = {103},
  number       = {1-4},
  pages        = {137--172},
  year         = {2010},
  url          = {https://doi.org/10.3233/FI-2010-323},
  doi          = {10.3233/FI-2010-323},
  timestamp    = {Fri, 18 Sep 2020 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/fuin/KusmierekB10.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/splc/SchaeferBBDT10,
  author       = {Ina Schaefer and
                  Lorenzo Bettini and
                  Viviana Bono and
                  Ferruccio Damiani and
                  Nico Tanzarella},
  editor       = {Jan Bosch and
                  Jaejoon Lee},
  title        = {Delta-Oriented Programming of Software Product Lines},
  booktitle    = {Software Product Lines: Going Beyond - 14th International Conference,
                  {SPLC} 2010, Jeju Island, South Korea, September 13-17, 2010. Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {6287},
  pages        = {77--91},
  publisher    = {Springer},
  year         = {2010},
  url          = {https://doi.org/10.1007/978-3-642-15579-6\_6},
  doi          = {10.1007/978-3-642-15579-6\_6},
  timestamp    = {Sat, 19 Oct 2019 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/conf/splc/SchaeferBBDT10.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:journals/corr/abs-1005-0522,
  author       = {Luca Roversi and
                  Luca Vercelli},
  editor       = {Patrick Baillot},
  title        = {Safe Recursion on Notation into a Light Logic by Levels},
  booktitle    = {Proceedings International Workshop on Developments in Implicit Computational
                  complExity, {DICE} 2010, Paphos, Cyprus, 27-28th March 2010},
  series       = {{EPTCS}},
  volume       = {23},
  pages        = {63--77},
  year         = {2010},
  url          = {https://doi.org/10.4204/EPTCS.23.5},
  doi          = {10.4204/EPTCS.23.5},
  timestamp    = {Wed, 12 Sep 2018 01:05:13 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1005-0522.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{DBLP:journals/corr/abs-1011-3668,
  author       = {Luca Roversi},
  title        = {Linear lambda calculus with explicit substitutions as proof-search
                  in Deep Inference},
  journal      = {CoRR},
  volume       = {abs/1011.3668},
  year         = {2010},
  url          = {http://arxiv.org/abs/1011.3668},
  eprinttype   = {arXiv},
  eprint       = {1011.3668},
  timestamp    = {Mon, 13 Aug 2018 01:00:00 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1011-3668.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{abstraction:europarworkshop:09,
	abstract = {We discuss how common problems arising with multi/many-core distributed architectures can be effectively handled through co-design of parallel/distributed programming abstractions and of autonomic management of non-functional concerns. In particular, we demonstrate how restricted parallel/distributed patterns (or skeletons) may be efficiently managed by rule-based autonomic managers. We discuss the basic principles underlying pattern+manager co-design, current implementations inspired by this approach and some results achieved with a proof-of-concept prototype.},
	address = {Las Palmas, Spain},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick},
	booktitle = {Euro-Par 2008 Workshops - Parallel Processing, Selected Papers},
	date-added = {2009-01-09 17:57:45 +0100},
	date-modified = {2009-06-26 16:12:56 +0200},
	doi = {10.1007/978-3-642-00955-6_46},
	isbn = {978-3-642-00954-9},
	month = apr,
	pages = {403--414},
	publisher = {Springer},
	series = {LNCS},
	title = {Co-design of distributed systems using skeletons and autonomic management abstractions},
	url = {http://calvados.di.unipi.it/storage/paper_files/2009_abstraction_workshopeuropar.pdf},
	volume = {5415},
	year = {2009},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2009_abstraction_workshopeuropar.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-00955-6_46}}

@inproceedings{beske:ipdps:09,
	abstract = {An approach to the management of non-functional concerns in massively parallel and/or distributed architectures that marries parallel programming patterns with autonomic computing is presented. The necessity and suitability of the adoption of autonomic techniques are evidenced. Issues arising in the implementation of autonomic managers taking care of multiple concerns and of coordination among hierarchies of such autonomic managers are discussed. Experimental results are presented that demonstrate the feasibility of the approach.},
	address = {Rome, Italy},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick},
	booktitle = {Proc. of Intl. Parallel \& Distributed Processing Symposium (IPDPS)},
	date-added = {2008-12-09 18:58:37 +0100},
	date-modified = {2025-05-24 09:15:21 +0200},
	doi = {10.1109/IPDPS.2009.5161034},
	keywords = {grid},
	month = may,
	pages = {1--12},
	publisher = {IEEE},
	title = {Autonomic management of non-functional concerns in distributed and parallel application programming},
	url = {http://calvados.di.unipi.it/storage/paper_files/2009_f_nf_IPDPS.pdf},
	year = {2009},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2009_f_nf_IPDPS.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/IPDPS.2009.5161034}}

@inproceedings{beske:pdp:09,
	abstract = {We address the issue of autonomic management in hierarchical component-based distributed systems. The long term aim is to provide a modeling framework for autonomic management in which QoS goals can be defined, plans for system adaptation described and proofs of achievement of goals by (sequences of) adaptations furnished. Here we present an early step on this path. We restrict our focus to skeleton-based systems in order to exploit their well-defined structure. The autonomic cycle is described using the Orc system orchestration language while the plans are presented as structural modifications together with associated costs and benefits. A case study is presented to illustrate the interaction of managers to maintain QoS goals for throughput under varying conditions of resource availability.},
	address = {Weimar, Germany},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick},
	booktitle = {Proc. of Intl. Euromicro PDP 2009: Parallel Distributed and network-based Processing},
	date-added = {2008-10-15 22:43:41 +0200},
	date-modified = {2025-05-24 09:15:30 +0200},
	doi = {10.1109/PDP.2009.48},
	editor = {Didier El Baz and Tom Gross and Francois Spies},
	keywords = {grid},
	month = feb,
	pages = {3--10},
	publisher = {IEEE},
	title = {Towards hierarchical management of autonomic components: a case study},
	url = {http://calvados.di.unipi.it/storage/paper_files/2009_hier_man_PDP.pdf},
	year = {2009},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2009_hier_man_PDP.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/PDP.2009.48}}

@techreport{fastflow:tr-09-12,
	abstract = {Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and quite hard to optimise; providing multi-core programmers with optimising tools and programming frameworks is a nowadays challenge. Few efforts have been done to support effective streaming applications on these architectures. In this paper we introduce FastFlow, a low-level programming framework based on lock-free queues explicitly designed to support high-level languages for streaming applications. We compare FastFlow with state-of-the-art programming frameworks such as Cilk, OpenMP, and Intel TBB. We experimentally demonstrate that FastFlow is always more efficient than all of them in a set of micro-benchmarks and on a real world application; the speedup edge of FastFlow over other solutions might be bold for fine grain tasks, as an example +35% on OpenMP, +226% on Cilk, +96% on TBB for the alignment of protein P01111 against UniProt DB using Smith-Waterman algorithm.},
	author = {Marco Aldinucci and Massimo Torquati and Massimiliano Meneghin},
	date-added = {2010-02-13 16:20:18 +0100},
	date-modified = {2013-11-24 00:39:38 +0000},
	institution = {Universit{\`a} di Pisa, Dipartimento di Informatica, Italy},
	keywords = {fastflow},
	month = sep,
	number = {TR-09-12},
	title = {{FastFlow}: Efficient Parallel Streaming Applications on Multi-core},
	url = {http://arxiv.org/abs/0909.1187},
	year = {2009},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/TR-09-12.pdf},
	bdsk-url-2 = {http://arxiv.org/abs/0909.1187}}

@inproceedings{semi-formal:fmco:09,
	abstract = {Functional and non-functional concerns require different programming effort, different techniques and different methodologies when attempting to program efficient parallel/distributed applications. In this work we present a ``programmer oriented'' methodology based on formal tools that permits reasoning about parallel/distributed program development and refinement. The proposed methodology is semi-formal in that it does not require the exploitation of highly formal tools and techniques, while providing a palatable and effective support to programmers developing parallel/distributed applications, in particular when handling non-functional concerns.},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick},
	booktitle = {Formal Methods for Components and Objects: 7th Intl. Symposium, FMCO 2008, Sophia-Antipolis, France, October 20 - 24, 2008, Revised Lectures},
	date-added = {2009-06-07 16:05:13 +0200},
	date-modified = {2025-05-24 09:24:46 +0200},
	doi = {10.1007/978-3-642-04167-9},
	editor = {Frank S. de Boer and Marcello M. Bonsangue and Eric Madelaine},
	keywords = {semantics, parallel},
	pages = {204--225},
	publisher = {Springer},
	series = {LNCS},
	title = {Semi-formal models to support program development: autonomic management within component based parallel and distributed programming},
	url = {http://calvados.di.unipi.it/storage/paper_files/2009_semiformal_FMCO08.pdf},
	volume = {5751},
	year = {2009},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2009_semiformal_FMCO08.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-04167-9}}

@inproceedings{stkm:europar:09,
	abstract = {This paper investigates an implementation of STKM, a Spatio-Temporal sKeleton Model. STKM expands the Grid Component Model (GCM) with an innovative programmable approach that allows programmers to compose an application by combining component, workflow and skeleton concepts. The paper deals with a projection of the STKM model on top of SCA and it evaluates its implementation using Tuscany Java SCA. Experimental results show the need and the benefits of the high level of abstraction offered by STKM.},
	address = {Delft, The Netherlands},
	author = {Aldinucci, Marco and Bouziane, Hinde Lilia and Danelutto, Marco and P{\'e}rez, Christian},
	booktitle = {Proc. of 15th Intl. Euro-Par 2009 Parallel Processing},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1007/978-3-642-03869-3},
	keywords = {grid},
	month = aug,
	pages = {678--690},
	publisher = {Springer},
	series = {LNCS},
	title = {{STKM} on {SCA}: a Unified Framework with Components, Workflows and Algorithmic Skeletons},
	url = {http://calvados.di.unipi.it/storage/paper_files/2009_STKM_Europar.pdf},
	volume = {5704},
	year = {2009},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2009_STKM_Europar.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-03869-3}}

@inproceedings{DBLP:conf/soco/BettiniBT09,
  author       = {Lorenzo Bettini and
                  Viviana Bono and
                  Erica Turin},
  editor       = {Alexandre Bergel and
                  Johan Fabry},
  title        = {I-Java: An Extension of Java with Incomplete Objects and Object Composition},
  booktitle    = {Software Composition - 8th International Conference, SC@TOOLS 2009,
                  Zurich, Switzerland, July 2-3, 2009. Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {5634},
  pages        = {27--44},
  publisher    = {Springer},
  year         = {2009},
  url          = {https://doi.org/10.1007/978-3-642-02655-3\_4},
  doi          = {10.1007/978-3-642-02655-3\_4},
  timestamp    = {Fri, 02 Jan 2026 21:20:37 +0100},
  biburl       = {https://dblp.org/rec/conf/soco/BettiniBT09.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/fopara/RoversiV09,
  author       = {Luca Roversi and
                  Luca Vercelli},
  editor       = {Marko C. J. D. van Eekelen and
                  Olha Shkaravska},
  title        = {A Local Criterion for Polynomial-Time Stratified Computations},
  booktitle    = {Foundational and Practical Aspects of Resource Analysis - First International
                  Workshop, {FOPARA} 2009, Eindhoven, The Netherlands, November 6, 2009,
                  Revised Selected Papers},
  series       = {Lecture Notes in Computer Science},
  volume       = {6324},
  pages        = {114--130},
  publisher    = {Springer},
  year         = {2009},
  url          = {https://doi.org/10.1007/978-3-642-15331-0\_8},
  doi          = {10.1007/978-3-642-15331-0\_8},
  timestamp    = {Tue, 14 May 2019 10:00:45 +0200},
  biburl       = {https://dblp.org/rec/conf/fopara/RoversiV09.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/lfcs/LagoRV09,
  author       = {Ugo Dal Lago and
                  Luca Roversi and
                  Luca Vercelli},
  editor       = {Sergei N. Art{\"{e}}mov and
                  Anil Nerode},
  title        = {Taming Modal Impredicativity: Superlazy Reduction},
  booktitle    = {Logical Foundations of Computer Science, International Symposium,
                  {LFCS} 2009, Deerfield Beach, FL, USA, January 3-6, 2009. Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {5407},
  pages        = {137--151},
  publisher    = {Springer},
  year         = {2009},
  url          = {https://doi.org/10.1007/978-3-540-92687-0\_10},
  doi          = {10.1007/978-3-540-92687-0\_10},
  timestamp    = {Fri, 27 Mar 2020 00:00:00 +0100},
  biburl       = {https://dblp.org/rec/conf/lfcs/LagoRV09.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/mfcs/GaboardiRV09,
  author       = {Marco Gaboardi and
                  Luca Roversi and
                  Luca Vercelli},
  editor       = {Rastislav Kr{\'{a}}lovic and
                  Damian Niwinski},
  title        = {A By-Level Analysis of Multiplicative Exponential Linear Logic},
  booktitle    = {Mathematical Foundations of Computer Science 2009, 34th International
                  Symposium, {MFCS} 2009, Novy Smokovec, High Tatras, Slovakia, August
                  24-28, 2009. Proceedings},
  series       = {Lecture Notes in Computer Science},
  volume       = {5734},
  pages        = {344--355},
  publisher    = {Springer},
  year         = {2009},
  url          = {https://doi.org/10.1007/978-3-642-03816-7\_30},
  doi          = {10.1007/978-3-642-03816-7\_30},
  timestamp    = {Tue, 14 May 2019 10:00:37 +0200},
  biburl       = {https://dblp.org/rec/conf/mfcs/GaboardiRV09.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@incollection{assist:juxmem:IW_book:07,
	abstract = {Enabling high-level programming models on grids is today a major challenge. A way to achieve this goal relies on the use of environments able to transparently and automatically provide adequate support for low-level, grid-specific issues (fault-tolerance, scalability, etc.). This paper discusses the above approach when applied to grid data management. As a case study, we propose a 2-tier software architecture that supports transparent, fault-tolerant, grid-level data sharing in the ASSIST programming environment (University of Pisa), based on the JuxMem grid data sharing service (INRIA Rennes).},
	address = {Krak{\'o}w, Poland},
	author = {Marco Aldinucci and Gabriel Antoniu and Marco Danelutto and Mathieu Jan},
	booktitle = {Achievements in European Research on Grid Systems},
	date-added = {2007-06-26 01:31:31 +0200},
	date-modified = {2024-04-01 11:29:47 +0200},
	doi = {10.1007/978-0-387-72812-4_6},
	editor = {Marian Bubak and Sergei Gorlatch and Thierry Priol},
	isbn = {978-0-387-72811-7},
	keywords = {parallel},
	month = nov,
	pages = {67--81},
	publisher = {Springer},
	series = {CoreGRID},
	title = {Fault-Tolerant Data Sharing for High-level Grid Programming: A Hierarchical Storage Architecture},
	url = {http://calvados.di.unipi.it/storage/paper_files/2007_IW06_book_juxadhocmem.pdf},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2007_IW06_book_juxadhocmem.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-0-387-72812-4_6}}

@article{assist:pepa:ppl:08,
	abstract = {Grid technologies aim to harness the computational capabilities of widely distributed collections of computers. Due to the heterogeneous and dynamic nature of the set of grid resources, the programming and optimisation burden of a low level approach to grid computing is clearly unacceptable for large scale, complex applications. The development of grid applications can be simplified by using high-level programming environments. In the present work, we address the problem of the mapping of a high-level grid application onto the computational resources. In order to optimise the mapping of the application, we propose to automatically generate performance models from the application using the process algebra PEPA. We target applications written with the high-level environment ASSIST, since the use of such a structured environment allows us to automate the study of the application more effectively.},
	annote = {ISSN: 0129-6264},
	author = {Marco Aldinucci and Anne Benoit},
	date-modified = {2024-04-01 11:30:51 +0200},
	doi = {10.1142/S0129626408003302},
	issn = {0129-6264},
	journal = {Parallel Processing Letters},
	keywords = {parallel},
	month = mar,
	number = {1},
	pages = {175--188},
	title = {Automatic mapping of {ASSIST} applications using process algebra},
	url = {http://calvados.di.unipi.it/storage/paper_files/2008_pepa_ppl.pdf},
	volume = {18},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2008_pepa_ppl.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1142/S0129626408003302}}

@incollection{beske:cg_book:08,
	abstract = {Autonomic management can improve the QoS provided by parallel/distributed applications. Within the CoreGRID Component Model, the autonomic management is tailored to the automatic -- monitoring-driven -- alteration of the component assembly and, therefore, is defined as the effect of (distributed)management code. This work yields a semantics based on hypergraph rewriting suitable tomodel the dynamic evolution and non-functional aspects of Service Oriented Architectures and component-based autonomic applications. In this regard, our main goal is to provide a formal description of adaptation operations that are typically only informally specified. We advocate that our approach makes easier to raise the level of abstraction of management code in autonomic and adaptive applications.},
	author = {Marco Aldinucci and Sonia Campa and Marco Danelutto and Patrizio Dazzi and Peter Kilpatrick and Domenico Laforenza and Nicola Tonellotto},
	booktitle = {Making Grids Work},
	chapter = {Component Programming Models},
	date-added = {2007-12-09 22:26:46 +0100},
	date-modified = {2024-04-01 11:29:39 +0200},
	doi = {10.1007/978-0-387-78448-9_1},
	editor = {Marco Danelutto and Paraskevi Frangopoulou and Vladimir Getov},
	isbn = {978-0-387-78447-2},
	keywords = {parallel},
	month = aug,
	pages = {3--16},
	publisher = {Springer},
	series = {CoreGRID},
	title = {Behavioural skeletons for component autonomic management on grids},
	url = {http://calvados.di.unipi.it/storage/paper_files/2007_beske_cg_crete_book.pdf},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2007_beske_cg_crete_book.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-0-387-78448-9_1}}

@inproceedings{orc:IW:08,
	abstract = {We present O2J, a Java library that allows implementation of Orc programs on distributed architectures including grids and clusters/networks of workstations. With minimal programming effort the grid programmer may implement Orc programs, as he/she is not required to write any low level code relating to distributed orchestration of the computation but only that required to implement Orc expressions. Using the prototype O2J implementation, grid application developers can reason about abstract grid orchestration code described in Orc. Once the required orchestration has been determined and its properties analysed, a grid application prototype can be simply, efficiently and quickly implemented by taking the Orc code, rewriting it into corresponding Java/O2J syntax and finally providing the functional code implementing the sites and processes involved. The proposed modus operandi brings a Model Driven Engineering approach to grid application development.},
	address = {Hersonissos, Crete, Greece},
	annote = {Duplicate},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick and Patrizio Dazzi},
	booktitle = {Proc. of the Integrated Research in Grid Computing Workshop},
	date-added = {2008-02-09 16:59:20 +0100},
	date-modified = {2024-08-18 14:45:22 +0200},
	editor = {Sergei Gorlatch and Paraskevi Fragopoulou and Thierry Priol},
	keywords = {grid},
	month = apr,
	pages = {2--13},
	series = {CoreGRID},
	title = {From {Orc} Models to Distributed Grid {Java} code},
	url = {http://calvados.di.unipi.it/storage/paper_files/2008_IW_O2J.pdf},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2008_IW_O2J.pdf}}

@inproceedings{orc:parco:07,
	abstract = {A framework supporting fast prototyping as well as tuning of distributed applications is presented. The approach is based on the adoption of a formal model that is used to describe the orchestration of distributed applications. The formal model (Orc by Misra and Cook) can be used to support semi-formal reasoning about the applications at hand. The paper describes how the framework can be used to derive and evaluate alternative orchestrations of a well know parallel/distributed computation pattern; and shows how the same formal model can be used to support generation of prototypes of distributed applications skeletons directly from the application description.},
	address = {Germany},
	annote = {Parco 2007},
	author = {Marco Aldinucci and Marco Danelutto and Peter Kilpatrick},
	booktitle = {Parallel Computing: Architectures, Algorithms and Applications},
	date-added = {2007-06-26 01:48:06 +0200},
	date-modified = {2024-08-18 14:45:22 +0200},
	editor = {C. Bischof and M. B{\"u}cker and P. Gibbon and G. R. Joubert and T. Lippert and B. Mohr and F. J. Peters},
	isbn = {9781586037963},
	keywords = {grid},
	pages = {235--242},
	publisher = {IOS press},
	series = {ADVANCES IN PARALLEL COMPUTING},
	title = {A framework for prototyping and reasoning about grid systems},
	url = {http://calvados.di.unipi.it/storage/paper_files/2007_orc_parco.pdf},
	volume = {15},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2007_orc_parco.pdf}}

@inproceedings{orc:pdp:08,
	abstract = {Autonomic management can be used to improve the QoS provided by parallel/distributed applications. We discuss behavioural skeletons introduced in earlier work: rather than relying on programmer ability to design "from scratch" efficient autonomic policies, we encapsulate general autonomic controller features into algorithmic skeletons. Then we leave to the programmer the duty of specifying the parameters needed to specialise the skeletons to the needs of the particular application at hand. This results in the programmer having the ability to fast prototype and tune distributed/parallel applications with non-trivial autonomic management capabilities. We discuss how behavioural skeletons have been implemented in the framework of GCM (the grid component model developed within the CoreGRID NoE and currently being implemented within the GridCOMP STREP project). We present results evaluating the overhead introduced by autonomic management activities as well as the overall behaviour of the skeletons. We also present results achieved with a long running application subject to autonomic management and dynamically adapting to changing features of the target architecture. Overall the results demonstrate both the feasibility of implementing autonomic control via behavioural skeletons and the effectiveness of our sample behavioural skeletons in managing the "functional replication" pattern(s).},
	address = {Toulouse, France},
	author = {Marco Aldinucci and Sonia Campa and Marco Danelutto and Marco Vanneschi and Patrizio Dazzi and Domenico Laforenza and Nicola Tonellotto and Peter Kilpatrick},
	booktitle = {Proc. of Intl. Euromicro PDP 2008: Parallel Distributed and network-based Processing},
	date-added = {2007-10-09 12:13:13 +0200},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1109/PDP.2008.46},
	editor = {Didier El Baz and Julien Bourgeois and Francois Spies},
	keywords = {grid},
	month = feb,
	pages = {54--63},
	publisher = {IEEE},
	title = {Behavioural skeletons in {GCM}: autonomic management of grid components},
	url = {http://calvados.di.unipi.it/storage/paper_files/2008_orc_PDP.pdf},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2008_orc_PDP.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1109/PDP.2008.46}}

@inproceedings{sca:cgsymph:08,
	abstract = {Hierarchical autonomic management of structured grid applications can be efficiently implemented using production rule engines. Rules of the form "precondition-to-action" can be used to model the behaviour of autonomic managers in such a way that the autonomic control and the application management strategy are kept separate. This simplifies the manager design as well as user customization of autonomic manager policies. We briefly introduce rule-based autonomic managers. Then we discuss an implementation of a GCM-like behavioural skeleton -- a composite component modelling a standard parallelism exploitation pattern with its own autonomic controller -- in SCA/Tuscany. The implementation uses the JBoss rules engine to provide an autonomic behavioural skeleton component and services to expose the component functionality to the standard service framework. Performance results are discussed and finally similarities and differences with respect to the ProActive-based reference GCM implementation are discussed briefly.},
	address = {Las Palmas, Spain},
	author = {Marco Aldinucci and Marco Danelutto and Giorgio Zoppi and Peter Kilpatrick},
	booktitle = {From Grids To Service and Pervasive Computing (Proc. of the CoreGRID Symposium 2008)},
	date-added = {2008-05-11 18:42:40 +0200},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1007/978-0-387-09455-7_1},
	editor = {Thierry Priol and Marco Vanneschi},
	isbn = {978-0-387-09454-0},
	keywords = {grid},
	month = aug,
	pages = {3--18},
	publisher = {Springer},
	series = {CoreGRID},
	title = {Advances in Autonomic Components {\&} Services},
	url = {http://calvados.di.unipi.it/storage/paper_files/2008_SCA_cgsymph.pdf},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2008_SCA_cgsymph.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-0-387-09455-7_1}}

@article{security:jsa:07,
	abstract = {Algorithmic skeletons have been exploited to implement several parallel programming environments, targeting workstation clusters as well as workstation networks and computational grids. When targeting non-dedicated clusters, workstation networks and grids, security has to be taken adequately into account in order to guarantee both code and data confidentiality and integrity. However, introducing security is usually an expensive activity, both in terms of the effort required to managed security mechanisms and in terms of the time spent performing security related activities at run time.We discuss the cost of security introduction as well as how some features typical of skeleton technology can be exploited to improve the efficiency code and data securing in a typical skeleton based parallel programming environment and we evaluate the performance cost of security mechanisms implemented exploiting state of the art tools. In particular, we take into account the cost of security introduction in muskel, a Java based skeletal system exploiting macro data flow implementation technology. We consider the adoption of mechanisms that allow securing all the communications involving remote, unreliable nodes and we evaluate the cost of such mechanisms. Also, we consider the implications on the computational grains needed to scale secure and insecure skeletal computations.},
	author = {Marco Aldinucci and Marco Danelutto},
	date-added = {2007-10-31 19:23:37 +0100},
	date-modified = {2024-04-01 11:31:14 +0200},
	doi = {10.1016/j.sysarc.2008.02.008},
	journal = {Journal of Systems Architecture},
	keywords = {parallel},
	month = sep,
	number = {9},
	pages = {868--876},
	publisher = {Elsevier},
	title = {Securing skeletal systems with limited performance penalty: the {Muskel} experience},
	url = {http://calvados.di.unipi.it/storage/paper_files/2008_security_JSA.pdf},
	volume = {54},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2008_security_JSA.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1016/j.sysarc.2008.02.008}}

@inproceedings{sem:cgsymph:08,
	abstract = {Autonomic management can improve the QoS provided by parallel/distributed applications. Within the CoreGRID Component Model, the autonomic management is tailored to the automatic -- monitoring-driven -- alteration of the component assembly and, therefore, is defined as the effect of (distributed)management code. This work yields a semantics based on hypergraph rewriting suitable tomodel the dynamic evolution and non-functional aspects of Service Oriented Architectures and component-based autonomic applications. In this regard, our main goal is to provide a formal description of adaptation operations that are typically only informally specified. We advocate that our approach makes easier to raise the level of abstraction of management code in autonomic and adaptive applications.},
	address = {Las Palmas, Spain},
	author = {Marco Aldinucci and Emilio Tuosto},
	booktitle = {From Grids To Service and Pervasive Computing (Proc. of the CoreGRID Symposium 2008)},
	date-added = {2008-05-11 18:46:45 +0200},
	date-modified = {2024-08-18 14:45:22 +0200},
	doi = {10.1007/978-0-387-09455-7_3},
	editor = {Thierry Priol and Marco Vanneschi},
	isbn = {978-0-387-09454-0},
	keywords = {grid},
	month = aug,
	pages = {31--45},
	publisher = {Springer},
	series = {CoreGRID},
	title = {Towards a Formal Semantics for Autonomic Components},
	url = {http://calvados.di.unipi.it/storage/paper_files/2008_sem_cgsymph.pdf},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2008_sem_cgsymph.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1007/978-0-387-09455-7_3}}

@inproceedings{stkm:CBHPC:08,
	abstract = {We explore the possibilities offered by a programming model supporting components, workflows and skeletons. In particular we describe how Stcm (Spatio-Temporal Component Model), an already existing programming model supporting components and workflows, can be extended to also provide algorithmic skeleton concepts. Programmers are therefore enabled to assembly applications specifying both temporal and spatial relations among components and instantiating predefined skeleton composite components to implement all those application parts that can be easily modeled with the available skeletons. We discuss preliminary results as well as the benefits deriving from Stkm (Spatio-Temporal sKeleton Model) adoption in a couple of real applications.},
	address = {New York, NY, USA},
	author = {Aldinucci, Marco and Danelutto, Marco and Bouziane, Hinde Lilia and P{\'e}rez, Christian},
	booktitle = {Proc. of the ACM SIGPLAN Component-Based High Performance Computing (CBHPC)},
	date-modified = {2025-05-24 09:18:59 +0200},
	doi = {10.1145/1456190.1456194},
	isbn = {978-1-60558-311-2},
	keywords = {grid},
	location = {Karlsruhe, Germany},
	month = oct,
	pages = {1--11},
	publisher = {ACM},
	title = {Towards Software Component Assembly Language Enhanced with Workflows and Skeletons},
	url = {http://calvados.di.unipi.it/storage/paper_files/2008_CBHPC.pdf},
	year = {2008},
	bdsk-url-1 = {http://calvados.di.unipi.it/storage/paper_files/2008_CBHPC.pdf},
	bdsk-url-2 = {http://dx.doi.org/10.1145/1456190.1456194}}