Projects

@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}}

@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}}

@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}
}

@misc{26:torino2033:ai,
	address = {Torino},
	author = {Marco Aldinucci},
	howpublished = {Torino 2033 - Candidatura a Capitale Europea della Cultura},
	keywords = {IT4LIA},
	month = feb,
	note = {Presentation, 24 February 2026},
	title = {Torino 2033},
	url = {https://datacloud.di.unito.it/index.php/s/CEgdigErzTpnBXy},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/CEgdigErzTpnBXy}}

@misc{26:pdp:streamflow,
	address = {Cluj-Napoca, Romania},
	author = {Marco Aldinucci},
	date-modified = {2026-09-19 19:46:34 +0200},
	howpublished = {PDP 2026 - 34th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing},
	keywords = {IT4LIA},
	month = mar,
	note = {Keynote talk},
	title = {Streaming Workflows: From Scientific Applications to {AI} and Back},
	url = {https://datacloud.di.unito.it/index.php/s/dHLPLE5NdACgdH8},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/dHLPLE5NdACgdH8}}

@misc{26:aihub:sustainablehpc4ai,
	address = {ITC-ILO, United Nations Campus - Torino},
	author = {Marco Aldinucci},
	howpublished = {Shaping AI Futures Across Africa and Europe - AI Hub for Sustainable Development},
	keywords = {IT4LIA},
	month = mar,
	title = {Sustainable HPC4AI},
	url = {https://datacloud.di.unito.it/index.php/s/yGTHXBYKsbNZAo6},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/yGTHXBYKsbNZAo6}}

@misc{26:innotech:aiscaling,
	address = {Milano},
	author = {Marco Aldinucci},
	howpublished = {InnoTech Community - The European House - Ambrosetti, hosted by NTT DATA},
	keywords = {IT4LIA},
	month = apr,
	title = {How AI Really Scales},
	url = {https://datacloud.di.unito.it/index.php/s/R3qDWTLMz9KRq2y},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/R3qDWTLMz9KRq2y}}

@misc{26:aibootcamp:hpcai,
	address = {Villa Campolieto - Ercolano (NA)},
	author = {Marco Aldinucci},
	howpublished = {AI Strategy Bootcamp - Sto{\`a}},
	keywords = {IT4LIA},
	month = apr,
	note = {Inspirational Talk},
	title = {{HPC-AI}: Mens Sana in Corpore Sano},
	url = {https://datacloud.di.unito.it/index.php/s/s6ocCfrY5Xfwj7w},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/s6ocCfrY5Xfwj7w}}

@misc{26:acw:it4lia,
	address = {Palazzo Gnudi - Bologna},
	author = {Daniele Cesarini},
	howpublished = {Advanced Computing Workshop 2026 - Dell Technologies and NVIDIA},
	keywords = {IT4LIA},
	month = may,
	title = {{CINECA IT4LIA}: The Most Powerful AI Supercomputer in Italy},
	url = {https://datacloud.di.unito.it/index.php/s/pwCMYZML2JyBYYM},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/pwCMYZML2JyBYYM}}

@misc{26:polito:infrastrutture,
	address = {Politecnico di Torino - Torino},
	author = {Marco Aldinucci},
	howpublished = {Workshop ENIA - Ente Nazionale per l'Intelligenza Artificiale},
	keywords = {IT4LIA},
	month = jul,
	title = {L'intelligenza artificiale nella progettazione e realizzazione delle grandi opere infrastrutturali},
	url = {https://datacloud.di.unito.it/index.php/s/WnsrkqecNzmWDaG},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/WnsrkqecNzmWDaG}}

@misc{25:fit:aifactory,
	address = {OGR - Torino},
	author = {Marco Aldinucci},
	keywords = {IT4LIA},
	month = may,
	title = {FIT - Futuro Innovazione Torinese},
	url = {https://www.eventbrite.com/e/biglietti-fit-futuro-innovazione-torinese-3-appuntamento-1354422487119?utm_experiment=test_share_listing&aff=ebdsshios},
	year = 2025,
	bdsk-url-1 = {https://www.eventbrite.com/e/biglietti-fit-futuro-innovazione-torinese-3-appuntamento-1354422487119?utm_experiment=test_share_listing&aff=ebdsshios}}

@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: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}}

@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{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{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}}

@misc{26:ictp:fl,
	abstract = {This slide deck presents a 10-hour immersive course on Federated Learning (FL) fundamentals, delivered by Gianluca Mittone at the Master in High-Performance Computing (MHPC) at SISSA, May 13th--15th, 2026. The course is organized into 10 hours of theoretical lectures and 10 hours of practical exercises. Theoretical lectures cover six modules: (1) Introduction to Federated Learning; (2) Problem Formulation; (3) Practical Algorithmic Design; (4) Evaluation of Federated Learning Systems; (5) Limits of Standard Practices; and (6) Privacy, Robustness, Fairness, Personalization, followed by final remarks. Practical sessions guide students in building an FL simulator in a single Python process, re-implementing it in a distributed way using PyTorch's distributed backend, and experimenting with an off-the-shelf FL framework (Flower, optional). The course is based on "A Field Guide to Federated Optimization" by Wang et al. (2021). Key topics include the mathematical formulation of FL, FedAvg and its generalizations (FedOpt), communication efficiency, client sampling, data and computational heterogeneity, privacy-preserving protocols (SecAgg, differential privacy), robustness against poisoning attacks, fairness, personalization, and system constraints for real-world deployments. The course emphasizes the distinction between cross-device and cross-silo FL settings throughout, providing a comprehensive foundation for researchers and practitioners entering the field.},
	address = {Trieste, Italy},
	author = {Mittone, Gianluca},
	howpublished = {Master in High-Performance Computing},
	keywords = {icsc, dare, fl},
	month = may,
	title = {Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/opkdwZBDepa3b4m},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/opkdwZBDepa3b4m}}

@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}}

@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}}

@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}}

@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{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{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.},
}

@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},
}

@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: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{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: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: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}}

@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{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{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}}

@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}}

@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{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_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}}

@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}}

@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}}

@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}}

@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: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}}

@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{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}}

@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}}

@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}}

@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: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}}

@misc{26:brescia:phd:admission:final:exam,
	address = {Turin, Italy},
	author = {Brescia, Lorenzo},
	howpublished = {{Presentation of the PhD admission to the final exam}},
	keywords = {icsc, confidential},
	month = oct,
	title = {{Admission to the final exam}},
	url = {https://datacloud.di.unito.it/index.php/s/ZmNa9nTjqBXT2nr},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ZmNa9nTjqBXT2nr}}

@misc{26:brescia:phd:talk,
	address = {Turin, Italy},
	author = {Brescia, Lorenzo},
	howpublished = {{Presentation of the PhD advancements}},
	keywords = {icsc, confidential},
	month = feb,
	title = {{TRUE Protection of Scientific Workflows}},
	url = {https://datacloud.di.unito.it/index.php/s/5smA8Ke9moQK2W9},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5smA8Ke9moQK2W9}}

@misc{26:unito:school,
	abstract = {This presentation introduces the research activities of the Alpha Parallel Computing Group at the University of Turin, affiliated with the ICSC National Research Centre in HPC, Big Data and Quantum Computing. It outlines three main research areas: parallel and cloud computing, federated learning, and large-scale complex systems. In parallel computing, the group covers equation-based simulation and high-performance numerical algorithms---such as the LSQR algorithm for the Gaia mission---fluid dynamics via Navier--Stokes equations for wind energy and motorsport, and energy-aware optimization across heterogeneous HPC architectures. In federated learning, it motivates privacy-preserving distributed learning under regulations like GDPR, presents the PRAISE medical use case, describes FL training/inference and parallel communication topologies (tree, master-worker, peer-to-peer), and demonstrates cross-facility federations with LLaMA-3 across the Leonardo, LUMI, and MeluXina HPC centres. Finally, for large-scale complex systems, it discusses bottom-up agent-based simulation and top-down graph-based modelling, with examples including the Internet and federated social networks such as Mastodon. The talk concludes with the group's expertise, collaborations, and open-source contributions.},
	address = {Turin, Italy},
	author = {Antelmi, Alessia and Mittone, Gianluca and Malenza, Giulio},
	howpublished = {Department of Computer Science - Orientation day for schools},
	keywords = {icsc, fl, analytics, hpc},
	month = jan,
	title = {Alpha Parallel Computing Group: la nostra ricerca},
	url = {https://datacloud.di.unito.it/index.php/s/ZHYDXr3Z95tNfWq},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ZHYDXr3Z95tNfWq}}

@misc{26:casella:FLCAopening,
	address = {Singapore},
	author = {Bruno Casella and Samuele Fonio and Mirko Polato and Linara Adilova and Michael Kamp},
	howpublished = {The 40th Annual AAAI Conference on Artificial Intelligence},
	keywords = {icsc},
	month = jan,
	title = {Federated Learning for Critical Applications (FLCA) Workshop Opening (AAAI2026)},
	url = {https://datacloud.di.unito.it/index.php/s/ndi9eqLtJk5Npp5},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ndi9eqLtJk5Npp5}}

@misc{26:malenza:Workflow_OpenFOAM,
	abstract = {Computational fluid dynamics (CFD) simulations based on OpenFOAM are widely adopted in both academic and industrial settings. However, their execution typically relies on rigid, monolithic bash scripts that limit portability, scalability, and efficient exploitation of heterogeneous computing resources. This work introduces a workflow-oriented execution model for OpenFOAM that enables more flexible simulation orchestration and improved utilization of heterogeneous architectures. As a case study, we reimplement the well-known OpenFOAM Motorbike tutorial---a benchmark for steady incompressible Reynolds-averaged Navier--Stokes (RANS) simulations of airflow around a motorcycle and rider. The original tutorial follows a strictly sequential execution model defined in a single Allrun script, encompassing mesh generation, parallel decomposition, solver execution, and result reconstruction. We translate this bash-based pipeline into the Common Workflow Language (CWL), a widely adopted, implementation-agnostic workflow specification, and execute it using StreamFlow. This workflow management system supports modular execution, flexible orchestration, and improved resource utilization across heterogeneous platforms. The original execution steps---ranging from environment setup and mesh preparation to parallel solver runs and post-processing---are reorganized into discrete CWL workflow components. The workflow is structured into three main stages: (i) prep, responsible for mesh preparation; (ii) decompose, handling mesh decomposition and refinement; and (iii) foam_run, executing potential flow initialization, the main solver, and mesh reconstruction. Each stage is encapsulated in reusable CWL components, enabling clear separation of concerns and simplified workflow reconfiguration. The resulting workflow is executed on CINECA systems using the official OpenFOAM-v2412 distribution deployed via Docker/Singularity. This study demonstrates how traditional OpenFOAM tutorials can be transformed into portable, reproducible, and extensible workflows. By decoupling simulation phases and explicitly exposing orchestration strategies, the proposed approach lays the groundwork for systematic investigation of performance portability and scalability on heterogeneous and distributed computing environments.},
	address = {Bologna, IT},
	author = {Giulio Malenza and Alberto Mulone and Daniele Ibba and Marco Edoardo Santimaria and Lorenzo Bosio and Iacopo Colonnelli and Robert Birke and Marco Aldinucci},
	date-modified = {2026-09-19 19:46:55 +0200},
	keywords = {icsc},
	month = mar,
	title = {Exploring workflow solutions on the {OpenFOAM} framework},
	url = {https://datacloud.di.unito.it/index.php/s/HwXHg7GicwHcx59},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/HwXHg7GicwHcx59}}

@misc{26:unito:school1,
	abstract = {This presentation explores HPC from research to innovation and the opportunities for improvement. It begins with an intuitive introduction to sequential and parallel computing, using analogies such as a tiramis{\`u} recipe to explain how problems are decomposed into sub-problems solved concurrently. It then discusses the purposes of parallel computing: strong scaling (solving a fixed problem faster) and weak scaling (solving a larger problem in constant time). The presentation highlights the historical and ongoing impact of HPC through examples like the Leonardo supercomputer at CINECA (the second most powerful in the EU and first for AI applications), HPC6 by Eni (among the world's top supercomputers), and the HPC4AI laboratory at the University of Turin (designed in 2017, anticipating the cloud-HPC model needed for GenAI, with 91% energy efficiency). It showcases scientific breakthroughs enabled by HPC, including black hole simulations for the film Interstellar and AlphaFold's protein folding prediction. The talk emphasizes the inevitable marriage of HPC and AI, illustrating how performance optimization can achieve near-peak efficiency (e.g., matrix multiplication reaching 42% of theoretical peak). It concludes with reflections on building the future through innovation, drawing parallels to Alessandro Volta's battery, and stressing the importance of research looking 5+ years ahead with public funding to maintain skills and drive economic impact.},
	address = {Turin, Italy},
	author = {Mittone, Gianluca and Aldinucci, Marco},
	howpublished = {Department of Computer Science - Orientation day for schools},
	keywords = {icsc, hpc},
	month = apr,
	title = {HPC, dalla ricerca all'innovazione},
	url = {https://datacloud.di.unito.it/index.php/s/Lz4zkmxdgMinonk},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/Lz4zkmxdgMinonk}}

@misc{26:europar:fldp,
	abstract = {This presentation, delivered by Gianluca Mittone and Marco Aldinucci (University of Turin) at the 32nd International European Conference on Parallel and Distributed Computing (Euro-Par 26, Pisa, August 27th, 2026), introduces FL+FSDP and FL+HSDP, hybrid schemes that accelerate sharded data parallelism at scale by integrating federated learning (FL) aggregations. The talk begins by motivating the work: foundation models (FMs) are driving an exponential growth in hardware demands, with model sizes, training datasets, and parallelism scales increasing by orders of magnitude, as shown by compute-trend analyses. It reviews the taxonomy of FM parallelization and distribution techniques, then focuses on Fully Sharded Data Parallelism (FSDP) and Hybrid Sharded Data Parallelism (HSDP), explaining how model shards are reconstructed through AllGather/ReduceScatter and how HSDP adds a replication dimension via AllReduce. The core contribution is relaxing mathematical coherency with sequential training through FL: between FL aggregations, models can diverge, but if data is IID, FL statistically converges as sequential training, with N (aggregation frequency) providing a knob to balance communication efficiency and coherency. The presentation formalizes communication costs for FSDP, HSDP, FL, FL+FSDP, and FL+HSDP, and shows how FL+HSDP models a three-tier communication hierarchy---sharding, replication, and federation groups---that maps naturally onto complex HPC network topologies like DragonFly+, exploiting different bandwidth levels (intra-node NVLink ~1.8 TB/s, inter-node InfiniBand ~100 GB/s, and inter-group links). Experimental results on Llama3.1-8B training over ~150M tokens on 512 A100 GPUs (128 nodes) of the Leonardo supercomputer show that FL+FSDP processes data up to 8.04× faster and achieves 4.48× lower evaluation perplexity than baseline sharded DP methods, without hyperparameter tuning. Key contributions include the hybrid communication scheme, interconnect-aware training, theoretical communication-cost analysis, and empirical validation. Future work includes extending FL as a fifth orthogonal parallelism axis, automated topology-aware configuration, elimination of manual tuning, and forward-recovery fault tolerance using Federation Groups as alive backups.},
	address = {Pisa, Italy},
	author = {Mittone, Gianluca and Aldinucci, Marco},
	howpublished = {32nd International European Conference on Parallel and Distributed Computing (EuroPar26)},
	keywords = {icsc, dyman, space, fl},
	month = aug,
	note = {Best Paper Award},
	title = {Accelerating sharded data parallelism at scale with federated learning},
	url = {https://datacloud.di.unito.it/index.php/s/CYHFFwStdqDt4Cj},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/CYHFFwStdqDt4Cj}}

@misc{26:AI:bottlenecks:chemistry,
	address = {Torino, Italy},
	author = {Marco Aldinucci},
	date-added = {2026-09-13 23:09:23 +0200},
	date-modified = {2026-09-19 19:50:10 +0200},
	howpublished = {Chemistry through the eyes of AI and ML Chemistry through the eyes of AI and ML},
	keywords = {ai, hpc, icsc, hpc4ai},
	month = {September},
	title = {{NextGen AI} infrastructures: a taxonomy of bottlenecks},
	url = {https://datacloud.di.unito.it/index.php/s/B8p37LEMaNndk8t},
	year = {2026},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/B8p37LEMaNndk8t}}

@misc{25:casella:TDXPDP,
	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 vulnerabilities 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 = {Torino, Italy},
	author = {Bruno Casella},
	howpublished = {33rd Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2025)},
	keywords = {icsc},
	month = mar,
	title = {A Performance Analysis of VM-based Trusted Execution Environments for Confidential Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/CFjA4sqYkbw9Bjy},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/CFjA4sqYkbw9Bjy}}

@misc{25:icsc:cpfwi,
	address = {Milano, Italy},
	author = {Iacopo Colonnelli},
	howpublished = {ICSC Spoke 1 Event},
	keywords = {icsc, streamflow},
	month = mar,
	title = {Cross-Platform Full Waveform Inversion (CPFWI)},
	url = {https://datacloud.di.unito.it/index.php/s/3kbBHf39RaFCA9X},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/3kbBHf39RaFCA9X}}

@misc{25:cineca:xffl,
	abstract = {Big-tech companies pre-train SOTA LLMs on special-purpose, private HPCs, while public research centres lack the resources to compete. We advocate a new take on large model training, e.g., LLMs, called xFFL, which leverages federated learning as an enabling technique to exploit geographically distributed computing power to bridge such digital divide. This work introduces a proof-of-concept federated training of LLaMA-3 8B on three EuroHPC Top500 facilities, proving the viability of leveraging cross-facility publicly available computational power to sustain SOTA LLM workloads.},
	address = {Bologna, Italy},
	author = {Mittone, Gianluca and Mulone, Alberto and Colonnelli, Iacopo and Birke, Robert and Aldinucci, Marco},
	howpublished = {AI & HPC User Forums and HPC User Day},
	keywords = {icsc, eupilot, space, fl, streamflow},
	month = mar,
	title = {Cross-Facility Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/oXoeHLytc9CZ5KR},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/oXoeHLytc9CZ5KR}}

@misc{25:brescia:phd:talk,
	address = {Turin, Italy},
	author = {Brescia, Lorenzo},
	howpublished = {{Presentation of the PhD advancements}},
	keywords = {icsc, confidential},
	month = mar,
	title = {{Advancements in Secure Cloud Computing with Trusted Execution Environments}},
	url = {https://datacloud.di.unito.it/index.php/s/xnZY7QxCfg2bS4M},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/xnZY7QxCfg2bS4M}}

@misc{25:cineca:unito:hpc4ai,
	address = {Bologna, Italy},
	author = {Marco Aldinucci},
	howpublished = {CINECA--Universit\`a di Torino},
	keywords = {hpc4ai, icsc, hpc, ai},
	month = mar,
	title = {{HPC4AI} e {S-HPC4AI}},
	year = 2025}

@misc{26:ictp:fl,
	abstract = {This slide deck presents a 10-hour immersive course on Federated Learning (FL) fundamentals, delivered by Gianluca Mittone at the Master in High-Performance Computing (MHPC) at SISSA, May 13th--15th, 2026. The course is organized into 10 hours of theoretical lectures and 10 hours of practical exercises. Theoretical lectures cover six modules: (1) Introduction to Federated Learning; (2) Problem Formulation; (3) Practical Algorithmic Design; (4) Evaluation of Federated Learning Systems; (5) Limits of Standard Practices; and (6) Privacy, Robustness, Fairness, Personalization, followed by final remarks. Practical sessions guide students in building an FL simulator in a single Python process, re-implementing it in a distributed way using PyTorch's distributed backend, and experimenting with an off-the-shelf FL framework (Flower, optional). The course is based on "A Field Guide to Federated Optimization" by Wang et al. (2021). Key topics include the mathematical formulation of FL, FedAvg and its generalizations (FedOpt), communication efficiency, client sampling, data and computational heterogeneity, privacy-preserving protocols (SecAgg, differential privacy), robustness against poisoning attacks, fairness, personalization, and system constraints for real-world deployments. The course emphasizes the distinction between cross-device and cross-silo FL settings throughout, providing a comprehensive foundation for researchers and practitioners entering the field.},
	address = {Trieste, Italy},
	author = {Mittone, Gianluca},
	howpublished = {Master in High-Performance Computing},
	keywords = {icsc, dare, fl},
	month = may,
	title = {Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/opkdwZBDepa3b4m},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/opkdwZBDepa3b4m}}

@misc{25:casella:sgx_sfo,
	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, additional security measures, at the cost of increasing runtimes, become necessary. The Trusted Execution Environment (TEE) approach offers 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.},
	address = {San Francisco, US},
	author = {Bruno Casella and Iacopo Colonnelli and Gianluca Mittone and Robert Birke and Walter Riviera and Antonio Sciarappa and Carlo Cavazzoni and Marco Aldinucci},
	howpublished = {IEEE Security and Privacy Workshops (SPW)},
	keywords = {icsc},
	month = may,
	title = {A Performance Analysis for Confidential Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/pzx75FGgP43YybF},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/pzx75FGgP43YybF}}

@misc{25:asdmsd:fl,
	abstract = {Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train models while keeping raw data local. This lecture introduces the fundamental principles of FL and examines their application to foundation models, with particular emphasis on privacy, communication efficiency, data and computational heterogeneity, and system-level constraints. FL is presented as an approach for learning across decentralized data sources without directly transferring sensitive information, using focused model updates that are aggregated by a coordinating server. Privacy can be further strengthened through techniques such as differential privacy and secure aggregation. The lecture discusses the main FL deployment settings, distinguishing cross-device learning, involving potentially millions of intermittently available clients, from cross-silo learning, where organizations are generally more stable and highly available. These settings introduce challenges related to non-IID data, heterogeneous computational resources, limited connectivity, client availability, and communication overhead. Federated optimization therefore requires careful coordination between local computation, communication rounds, privacy requirements, and system constraints. Federated Averaging (FedAvg) is presented as a common optimization approach in which locally trained model parameters are periodically aggregated to produce a shared global model. A particular focus is placed on Cross-Facility Federated Learning (xFFL), which extends FL to geographically distributed HPC infrastructures. The approach uses hierarchical orchestration through StreamFlow, HPC-adapting middleware, and containerized PyTorch deployments to address differences in hardware, software environments, and infrastructure policies. Experiments with LLaMA-2 and LLaMA-3 demonstrate the application of federated learning to large language model workloads across HPC facilities. Overall, FL for foundation models is characterized as an interdisciplinary problem combining machine learning, optimization, privacy, security, cryptography, information theory, and distributed systems. The slides emphasize that effective FL systems require co-design of optimization algorithms with privacy, security, communication, computational, and deployment constraints, while careful evaluation is necessary to distinguish simulation results from behavior in real-world federated environments.},
	address = {Trieste, Italy},
	author = {Mittone, Gianluca},
	howpublished = {ICTP Advanced School on Foundation Models for Scientific Discovery},
	keywords = {icsc, dyman, fl},
	month = jul,
	title = {Federated Learning for Foundation Models},
	url = {https://datacloud.di.unito.it/index.php/s/5gzSkSgN8nzKz9D},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5gzSkSgN8nzKz9D}}

@misc{25:brescia:systex,
	address = {Venice, Italy},
	author = {Lorenzo Brescia},
	keywords = {icsc, confidential},
	month = jul,
	title = {{End-to-End Confidentiality with SEV-SNP Leveraging In-Memory Storage}},
	url = {https://datacloud.di.unito.it/index.php/s/R7EM3GCwmHRJJCq},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/R7EM3GCwmHRJJCq}}

@misc{25:casella:drocks,
	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.},
	address = {Porto, Portugal},
	author = {Bruno Casella and Matthias Jakobs and Marco Aldinucci and Sebastian Buschjaeger},
	howpublished = {3rD Workshop on Advancements in Federated Learning (ECML-PKDD)},
	keywords = {icsc},
	month = sep,
	title = {Decentralized Time Series Classification with ROCKET features},
	url = {https://datacloud.di.unito.it/index.php/s/Bp7Y5NmDDBwcizH},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/Bp7Y5NmDDBwcizH}}

@misc{25:riken:ftfl5,
	abstract = {This fourth periodic meeting presentation (October 28, 2025) reports on simulating full-scale LLM pre-training to study fault tolerance through FL. It introduces discrete-event and agent-based simulation, implemented with SimPy. Each GPU is modeled as an agent with lifecycle stages---Setup, Compute, Checkpoint, and Wait---organized into groups. Simulation parameters include setup time (~20 min), compute time (~1 s per sample), checkpoint interval (~10 min) and checkpoint time (~1 min), stochastic failure probability (~1e-5 per time step), and downtime (~1 h), each with 10% Gaussian variance. Preliminary results compare FSDP, FL-FR (forward recovery), and FL-FS (fail-stop) across node counts (4 GPUs per node). Metrics include overall execution time, setup time, checkpoint count, cumulative checkpointing time, failure count, and cumulative failure time. FL-FS generally reduces checkpointing overhead and failure impact at scale. Next steps include modeling setup and checkpoint failures, using real-world failure rates, simulating different convergence rates, estimating optimal checkpoint latency, and moving time steps to minutes.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: Fourth Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/mbWsNzAkmWDbdke},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/mbWsNzAkmWDbdke}}

@misc{25:riken:ftfl4,
	abstract = {This third periodic meeting presentation (October 14, 2025) presents a bottom-up approach to fault tolerance in large-scale ML training through FL. It describes the pre-training hardware/software stack---AI algorithms, frameworks, toolkits, and hardware---and how node-level complexity leads to noisy fault manifestations. It reviews failure frequency and cost, using pie charts from GPT-3 and Platform-X/Unicorn, and categorizes faults by severity (SEV1--SEV3). The talk details NCCL socket retry parameters (NCCL_SOCKET_RETRY_CNT, NCCL_SOCKET_RETRY_SLEEP_MSEC) and NCCL communicator shrinking via ncclCommShrink with NCCL_SHRINK_ABORT. It then presents xFFL deployment scenarios for a single sequential client per node and for distributed multi-node clients, showing two recovery approaches when a node crashes: shrink the NCCL communicator to exclude the failed node, or rebuild the distributed backend to exclude the client. If a node rejoins, the backend is rebuilt and other peers act as alive checkpoints. It compares PyTorch init_process_group and NCCL shrink setup times, advocating framework-handled fault tolerance with minimal user code and low impact on training performance.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: Third Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/iokaxmMcyy76B46},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/iokaxmMcyy76B46}}

@misc{25:riken:ftfl3,
	abstract = {This second periodic meeting presentation (September 30, 2025) focuses on fault tolerance for large-scale ML training through FL, with emphasis on LLM pre-training. It contrasts pre-training (huge unlabeled corpora, thousands of GPUs, weeks/months, high failure risk) and fine-tuning (small labeled datasets, few GPUs, hours/days, manageable risk). The talk presents xFFL's goal to make pre-training less communication-bound via intra-silo FedAvg, including layer-wise and bucket-wise aggregation. It analyzes failure scenarios using GPT-3 pre-training statistics on Alibaba Cloud, distinguishing transient faults (~73% of errors) from hardware faults (~27%) and their downtime costs. Current fault-tolerance approaches are reviewed: checkpointing, TorchSnapshot, SpikingBrain/DLRover Flash Checkpoint, Torchrun elasticity, pipeline-parallelism templates (Bamboo/Ooblack), and Gemini. Challenges include FL's extra redundancy, dynamic restart, zero-redundancy checkpointing, and communicator recreation. The proposed approach is framework-handled fault tolerance using NCCL low-level features---ncclCommAbort, ncclCommShrink, and socket retry parameters---to move from a fail-stop model toward retry and autonomous recovery, reserving rollback as a last resort.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: Second Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/AWPR5ASwwZt6gJ7},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/AWPR5ASwwZt6gJ7}}

@misc{25:riken:ftfl1,
	abstract = {This first periodic meeting presentation, held at RIKEN R-CCS (September 9, 2025), reviews fault tolerance in large-scale machine learning training through federated learning. It outlines the current xFFL implementation, which supports two deployment typologies: cross-facility (inter-cluster) FL using a master-worker topology coordinated by StreamFlow with built-in retry-rollback fault tolerance, and single-facility (intra-cluster) FL using peer-to-peer NCCL all-reduce, which still lacks fault tolerance. The talk discusses checkpointing and rollback strategies for centralized and decentralized FL, including server-side, client-side, individual, and collective checkpointing. It surveys fault-tolerant communication libraries---OpenMPI/MPICH with ULFM, MVAPICH2 checkpoint/restart features, and NCCL communicator abort/shrink capabilities---and covers PyVerbs for RDMA and Torchrun for elastic distributed training. Key open issues include assumptions of a reliable server, fail-stop client models, and the need for more flexible fault-tolerant communication. The presentation sets the stage for integrating fault tolerance directly into xFFL.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: First Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/W9J2i5PnZs5DiJk},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/W9J2i5PnZs5DiJk}}

@misc{25:riken:ftfl0,
	abstract = {This kick-off presentation, delivered by Gianluca Mittone at RIKEN R-CCS (Kobe, Japan, September 2, 2025), introduces three innovative distributed intelligence approaches for pushing federated learning (FL) boundaries. After an overview of the speaker's academic and research background, the talk motivates FL through privacy regulations and the standard FedAvg algorithm. It then presents three main contributions: (1) Model-Agnostic Federated Learning (MAFL/OpenFL-x), which extends OpenFL to support traditional machine learning models via AdaBoost.F, enabling non-deep model federation; (2) High-Performance Federated Learning (FastFL), a FastFlow/RISC-pb2l-based backend supporting multiple communication topologies---master-worker, peer-to-peer, and tree---with strong and weak scaling on HPC and heterogeneous microarchitectures; and (3) Cross-Facility Federated Learning (xFFL), which uses StreamFlow to coordinate federated training across multiple HPC facilities, demonstrated with LLaMA-2/3 pre-training across Leonardo, LUMI, and MeluXina. The talk concludes with future work on RDMA-based fault tolerance and extra applications such as edge-distributed multiview detection and drug-target interaction.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Pushing Federated Learning Boundaries: Three Innovative Distributed Intelligence Approaches},
	url = {https://datacloud.di.unito.it/index.php/s/Z9ZD2c3MPya2CS3},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/Z9ZD2c3MPya2CS3}}

@misc{25:escience:bookedslurm,
	abstract = {Modern scientific discovery is frequently backed by large-scale scientific experiments, which cannot prescind from the unparalleled computational capabilities provided by high-performance 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 user-focused extension of the Slurm workload manager, aiming to democratize HPC access across interdisciplinary research domains. BookedSlurm enables a partially decentralized regulation of fine-grained advanced resource reservations through a novel credit-based 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 web-based dashboard, enhancing system usability for novice and expert users. While the dashboard simplifies interactions for non-specialists, advanced users can directly access agent-level APIs for more complex and automated operations. The effectiveness of BookedSlurm is validated on a real-world bioinformatics use case from the SUS-MIRRI.IT project, showcasing its ability to enhance usability, optimize job scheduling, and streamline execution workflows.},
	address = {Chicago, IL, USA},
	author = {Sandro Gepiro Contaldo and Lorenzo Bosio and Janneth Estefania Hoyos Rea and Elisa Li Perottino and Sergio Rabellino and Marco Aldinucci and Marco Beccuti and Iacopo Colonnelli},
	howpublished = {21st IEEE International Conference on e-Science},
	keywords = {icsc},
	month = sep,
	title = {{BookedSlurm}: meeting user needs for advanced resource reservations in {Slurm}},
	url = {https://datacloud.di.unito.it/index.php/s/xT58tKdAjJDKy59},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/xT58tKdAjJDKy59}}

@misc{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 lambda, 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.},
	address = {Bologna, Italy},
	author = {Bruno Casella and Samuele Fonio and Lorenzo Sciandra and Claudio Gallicchio and Marco Aldinucci and Mirko Polato and Roberto Esposito},
	howpublished = {28th European Conference on Artificial Intelligence},
	keywords = {icsc},
	month = oct,
	title = {Fed2RC: Federated Rocket Kernels and Ridge Classifier for Time Series Classification},
	url = {https://datacloud.di.unito.it/index.php/s/CKpYNTrZ3KDgkc6},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/CKpYNTrZ3KDgkc6}}

@misc{25:iulm:hpcai,
	address = {Milano, Italy},
	author = {Marco Aldinucci},
	howpublished = {Economia e Management nell'era dell'Intelligenza Artificiale, Universit\`a IULM},
	keywords = {icsc, hpc4ai, ai},
	month = oct,
	title = {{HPC-AI}, dalla ricerca all'innovazione},
	year = 2025}

@misc{25:ibm:unito,
	address = {Torino, Italy},
	author = {Marco Aldinucci},
	howpublished = {{IBM}--Universit\`a di Torino},
	keywords = {hpc4ai, icsc, hpc, ai},
	month = oct,
	title = {Parallel Computing \@ {UNITO}},
	year = 2025}

@misc{25:huawei:fl,
	address = {Zurich, Switzerland},
	author = {Marco Aldinucci and Gianluca Mittone and Bruno Casella and Robert Birke},
	howpublished = {Huawei Snow Mountain 2025},
	keywords = {icsc, hpc4ai, fl},
	month = nov,
	title = {Decentralized Machine Learning and the Compute Continuum},
	year = 2025}

@misc{24:HiPEAC:riscv,
	abstract = {AI-focused RISC-V-based hardware accelerators},
	address = {Garching bei M{\"u}nchen, M{\"u}nchen, Germany},
	author = {Mittone, Gianluca},
	howpublished = {High Performance, Edge And Cloud computing Conference 2024 (HiPEAC 2024)},
	keywords = {eupilot, icsc},
	month = jan,
	title = {RISC-V for AI},
	url = {https://datacloud.di.unito.it/index.php/s/rFtxT7zryoKNGbP},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/rFtxT7zryoKNGbP}}

@misc{24:icsc:spoke1:ifab,
	abstract = {Presentation about the Innovation Grant in collaboration with IFAB},
	address = {Torino, Italy},
	author = {Robert Birke},
	howpublished = {ICSC - Spoke 1 meeting},
	keywords = {icsc, ai},
	month = feb,
	title = {{FLaaS}: Federated Learning as a Service},
	url = {https://datacloud.di.unito.it/index.php/s/yHXdTnC8xEqoJ6Y},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/yHXdTnC8xEqoJ6Y}}

@misc{24:icsc:spoke1:eni,
	abstract = {Presentation about the Innovation Grant in collaboration with ENI},
	address = {Torino, Italy},
	author = {Mulone, Alberto},
	howpublished = {ICSC - Spoke 1 meeting},
	keywords = {icsc, streamflow},
	month = feb,
	title = {Cross-Platform Full Waveform Inversion},
	url = {https://datacloud.di.unito.it/index.php/s/M3HkxA5wsBPS5ro},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/M3HkxA5wsBPS5ro}}

@misc{24:pdp:karmarkartalk,
	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 = {Dublin, Irelans},
	author = {Malenza, Giulio and Santimaria, Marco Edoardo},
	keywords = {icsc, hpc},
	month = mar,
	title = {Benchmarking Parallelization Models through Karmarkar`s algorithm},
	url = {https://datacloud.di.unito.it/index.php/s/JjKcAJpYS7ctX9r},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/JjKcAJpYS7ctX9r}}

@misc{24:alpha:unito,
	author = {Marco Aldinucci},
	howpublished = {University of Torino},
	keywords = {hpc4ai, icsc, hpc, ai, fl, streamflow},
	month = mar,
	title = {Parallel Computing at the University of Torino},
	year = 2024}

@misc{23:brescia:wide:talk,
	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.},
	address = {Athens, Greece},
	author = {Brescia, Lorenzo},
	howpublished = {{P}roceedings of the 2nd {W}orkshop on {W}orkflows in {D}istributed {E}nvironments},
	keywords = {icsc, confidential},
	month = apr,
	title = {{S}ecure {G}eneric {R}emote {W}orkflow {E}xecution with {TEE}s},
	url = {https://datacloud.di.unito.it/index.php/s/Prxq6EWGbcN8sWx},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/Prxq6EWGbcN8sWx}}

@misc{24:amulone:comete,
	address = {Torino, Italy},
	annote = {pitch talk},
	author = {Mulone, Alberto},
	howpublished = {COMETE PhD Workshop},
	keywords = {icsc},
	month = apr,
	title = {Workflows for future High-Performance Computing},
	url = {https://datacloud.di.unito.it/index.php/s/ZGG8fLMp5B7qRHS},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ZGG8fLMp5B7qRHS}}

@misc{24:icolonne:cwl4hpccern,
	address = {CERN, Meyrin, Switzerland},
	author = {Colonnelli, Iacopo},
	howpublished = {Workshop on workflow languages for {HEP} analysis},
	keywords = {across, eupex, space, icsc, streamflow},
	month = apr,
	title = {CWL in the HPC Ecosystem},
	url = {https://datacloud.di.unito.it/index.php/s/PRmqdwWHt6P2PH7},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/PRmqdwWHt6P2PH7}}

@misc{24:icolonne:ictp,
	abstract = {Hybrid workflow abstractions allow users to quickly design and orchestrate cross-facility workloads, decoupling tasks from environment-specific technical details to reduce complexity and increase reusability. Plus, workflow descriptions help ensure the reproducibility of scientific experiments through prospective and retrospective provenance collection. This module has been designed to provide a hands-on exploration of scientific workflows from various angles, from the initial design phase to their orchestration at extreme scales. We will use the practical example of the CommonWorkflow Language (CWL) open standard to demonstrate how workflows can be written, and the StreamFlow workflow system to execute them seamlessly on the CINECA HPC facility. We will also delve into the integration between scientific workflows and Jupyter Notebooks, which aims to give data scientists a familiar interface to scientific workflows. In this module, students will gain a comprehensive understanding of scientific workflows. They will learn how to use these workflows to model and orchestrate Machine Learning and Deep Learning pipelines. Additionally, they will explore how modern workflow management systems can efficiently scale data-oriented workloads from a researcher's laptop to an entire HPC facility.},
	address = {Trieste, Italy},
	author = {Iacopo Colonnelli},
	keywords = {icsc, streamflow, jupyter-workflow},
	month = may,
	title = {Dynamic hybrid workflows for Deep Learning on HPC infrastructure},
	url = {https://datacloud.di.unito.it/index.php/s/EaFHJEKNbW5oXeq},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/EaFHJEKNbW5oXeq}}

@misc{24:mittone:ictp,
	abstract = {Machine Learning (ML) is the branch of Artificial Intelligence focused on developing algorithms capable of adapting and improving their predictive or generative performance by feeding on data. Adapting or improving the system's behaviour based on the provided data is called learning since it is similar to the human learning process in many aspects. The same ML algorithm, usually referred to as a model, trained on different data will thus expose different capabilities and can, therefore, solve different tasks. FL is a relatively recent distributed ML methodology aiming to bridge the gap between the need to train ever bigger ML models on ever larger datasets and the individual and companies' will to protect and not share their private data. From another point of view, FL is also a way to distribute the training of an ML model even more than before. However, it should be considered that the learning performance of FL is usually lower than that of traditional centralised learning. This course will start from Kairouz ad McMahan's definition of FL: ''Federated learning is a machine learning setting where multiple entities (clients) collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client's raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.'' From this starting point, the most significant aspects of FL will be exposed and discussed. This tutorial will particularly explore FL from both the learning and computational [5] performance perspectives, investigating its pros and cons in a distributed ML setting. Since FL natively targets data privacy, some insights on how the FL process can be attacked and protected will also be discussed from a high-level perspective. Finally, a hands-on session will guide the participants in building a basic FL system, providing a better understanding of the major implementational difficulties of such a technique.},
	address = {Trieste, Italy},
	author = {Gianluca Mittone},
	keywords = {icsc, fl},
	month = may,
	title = {Into to Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/nSwwmedjqe2jbWJ},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/nSwwmedjqe2jbWJ}}

@misc{24:icolonne:cwlcon2024,
	abstract = {This presentation introduces the new CWL Working Groups initiative, describing what a Working Group actually is, which Working Groups already exist in the CWL community, and how anybody can create a new officially recognized Working Group. Then, the presentation will explore the CWL4HPC Working Group, using it as an example of how a CWL Working Group can actually work.},
	address = {Amsterdam, Netherlands},
	author = {Iacopo Colonnelli},
	howpublished = {2024 CWL Conference},
	keywords = {icsc},
	month = may,
	title = {{CWL} Working Groups},
	url = {https://datacloud.di.unito.it/index.php/s/zZDKdL8deLd4jSi},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/zZDKdL8deLd4jSi}}

@misc{24:brescia:hpc:school:talk,
	address = {Trento, Italy},
	author = {Brescia, Lorenzo},
	howpublished = {{Mentoring session: HPC Summer School, university of Trento}},
	keywords = {icsc, confidential},
	month = jun,
	title = {{Towards Secure WMS with TEEs}},
	url = {https://datacloud.di.unito.it/index.php/s/eAxEgqiTsGSRQz4},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/eAxEgqiTsGSRQz4}}

@misc{24:ic:elise:xffl,
	address = {Helsinki, Finland},
	author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Marco Aldinucci},
	keywords = {icsc,space,eupex},
	month = jun,
	note = {Invited talk},
	title = {Cross-Facility Federated Learning - Part II},
	url = {https://datacloud.di.unito.it/index.php/s/7HonBpcWPxotXLX},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/7HonBpcWPxotXLX}}

@misc{24:ma:hpcai:talk,
	abstract = {The talk presents the motivation and the activity of the "Software and Integration" lab at UNITO.},
	address = {Torino, Italy},
	author = {Marco Aldinucci},
	howpublished = {HPC as an enabling platform for AI event},
	keywords = {icsc,ai,hpc},
	month = jun,
	title = {From HPC4AI to Software & Integration living lab to innovation},
	url = {https://datacloud.di.unito.it/index.php/s/3SS3Xa9XorN6D9o},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/3SS3Xa9XorN6D9o}}

@misc{24:ma:swi:lab,
	abstract = {The presentation describes the main the activity of the "Software and Integration" lab at UNITO across its main flagship codes.},
	address = {Torino, Italy},
	author = {Marco Aldinucci},
	howpublished = {HPC as an enabling platform for AI event},
	keywords = {icsc,ai,hpc},
	month = jun,
	title = {Software & Integration lab of FutureHPC spoke},
	url = {https://datacloud.di.unito.it/index.php/s/WDjyXCGyYFJDQSd},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/WDjyXCGyYFJDQSd}}

@misc{24:gmalenza:hlpp:MPRISC-v,
	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 parallel applications on new architectures to harness their maximum performance fully remains a challenge. In this study, we benchmark 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 cache hit ratios stemming from model parallelism to split and assign parts of the model to specific (groups of) cores using 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 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.},
	address = {Pisa, Italy},
	author = {Giulio Malenza},
	keywords = {icsc,eupilot},
	month = jul,
	title = {Preliminary analysis of model parallelism applications on a 64-core RV64 Server CPU},
	url = {https://datacloud.di.unito.it/index.php/s/JrWwKALeaFEJSQo},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/JrWwKALeaFEJSQo}}

@misc{24:santimaria:hlpp:capiocl,
	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.},
	address = {Pisa, Italy},
	author = {Marco Edoardo Santimaria},
	date-modified = {2026-09-19 19:50:32 +0200},
	keywords = {icsc,capio,across,eupex,admire},
	month = jul,
	title = {{CAPIO-CL}: Cross Application Programmable IO - Coordination Language},
	url = {https://datacloud.di.unito.it/index.php/s/zsKY3PWzX5NFCiX},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/zsKY3PWzX5NFCiX}}

@misc{24:madrid:hipes:talk,
	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},
	author = {Alberto Mulone and Doriana Medi\'{c} and Marco Aldinucci},
	howpublished = {1st workshop about {H}igh-{P}erformance e-{S}cience (HiPES)},
	keywords = {streamflow, icsc, eupex},
	month = aug,
	title = {A Fault Tolerance mechanism for Hybrid Scientific Workflows},
	url = {https://datacloud.di.unito.it/index.php/s/9Ddj6fGgmDbLDXj},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/9Ddj6fGgmDbLDXj}}

@misc{24:icolonne:wscc,
	abstract = {Thanks to their generality, workflow models represent a powerful abstraction for designing complex applications and executing them on large-scale distributed architectures. However, several additional challenges appear when transitioning from cloud/HPC environments to the entire compute continuum. Continuum execution environments are fully distributed and modular, and modules can be heterogeneous and independent of each other. In addition, continuum workflows often rely on multiple intercommunicating agents that form complex micro-services architectures. Different agents deal with different communication and parallelization paradigms: network-based stream processing at the edge and file-based batch processing on HPC facilities. Finally, support for efficient interactive workflows in the continuum remains an open research problem. This talk explores these challenges and provides insights on how to deal with them. A ready-to-use software library accompanies each proposed solution to facilitate the reproducibility and reusability of the presented concepts.},
	address = {Madrid, Spain},
	author = {Iacopo Colonnelli},
	keywords = {icsc},
	month = aug,
	note = {Keynote Talk},
	title = {Scientific Workflows in the Continuum Era},
	url = {https://datacloud.di.unito.it/index.php/s/PkqYA3p38XLKgrt},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/PkqYA3p38XLKgrt}}

@misc{24:garcia:bigHPC:talk,
	address = {Pisa, Italy},
	author = {Marques, Adriano Garcia},
	howpublished = {{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}},
	keywords = {icsc, confidential},
	month = sep,
	title = {{Assessing Large Language Models Inference Performance on a 64-core RISC-V CPU with Silicon-Enabled Vectors}},
	url = {https://datacloud.di.unito.it/index.php/s/eN6Z62RQr2QsRYa},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/eN6Z62RQr2QsRYa}}

@misc{24:brescia:bigHPC:talk,
	address = {Pisa, Italy},
	author = {Brescia, Lorenzo},
	howpublished = {{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}},
	keywords = {icsc, confidential},
	month = sep,
	title = {{Performance Analysis on DNA Alignment Workload with Intel SGX Multithreading}},
	url = {https://datacloud.di.unito.it/index.php/s/wqgYiKpHBw5zbSa},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/wqgYiKpHBw5zbSa}}

@misc{24:icolonne:ICSC,
	address = {Roma, Italy},
	author = {Iacopo Colonnelli},
	keywords = {icsc},
	month = sep,
	title = {Scientific Workflows in the Heterogeneous Computing Era},
	url = {https://datacloud.di.unito.it/index.php/s/CyxiWsDbdg6rbpQ},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/CyxiWsDbdg6rbpQ}}

@misc{24:gmalenza:BigHPC2024,
	abstract = {High-performance computing and artificial intelligent simulations necessitate the rapid processing of large quantities of data. To handle such data volumes efficiently, leveraging the parallelism inherent in algorithms is crucial. Consequently, parallel programming frameworks have been developed to fully exploit modern parallel architectures. Among these, C++ PSTL stands out for its user-friendliness, portability, and high performance. In this study, we introduce a back-end for the PSTL implemented using the FastFlow parallel programming framework. We will evaluate correctness and performance of the back-end comparing results with other coming from traditional vendor-dependent back-ends like TBB and nvc++. Performance metrics are derived from running the LULESH application on both RISC-V and ARM architectures. Our results indicate that all three back-ends deliver comparable performance.},
	address = {Pisa, Italy},
	author = {Malenza, Giulio},
	keywords = {icsc},
	month = sep,
	title = {Exploiting C++ Parallel Algorithms through FastFlow},
	url = {https://datacloud.di.unito.it/index.php/s/GcpQ8cz9BRyM85B},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/GcpQ8cz9BRyM85B}}

@misc{24:gmalenza:scihpcexa,
	abstract = {In today's era of rapid technological advancement, artificial intelligence (AI) applications require large-scale, high-performance, and data-intensive computations, leading to significant energy demands. Addressing this challenge necessitates a combined approach involving both hardware and software innovations. Hardware manufacturers are developing new, efficient, and specialized solutions, with the RISC-V architecture emerging as a prominent player due to its open, extensible, and energy-efficient instruction set architecture (ISA). Simultaneously, software developers are creating new algorithms and frameworks, yet their energy efficiency often remains unclear. In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. Specifically, we examine the energy consumption of deep learning inference models across various AI frameworks. By comparing the performance of different frameworks, we aim to provide a detailed understanding of how these frameworks can optimize energy consumption on this architecture.},
	address = {Pisa, Italy},
	author = {Malenza, Giulio},
	keywords = {icsc, DYMAN, ai},
	month = sep,
	title = {Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU},
	url = {https://datacloud.di.unito.it/index.php/s/5aTdyzNB6n9CREq},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5aTdyzNB6n9CREq}}

@misc{24:santimaria:bighpc,
	abstract = {CAPIO is a middleware that transparently injects streaming capabilities into file-based workflows. However, its implementation is limited to HPC environments based on the MPI framework, significantly limiting its applications. This paper will illustrate a proposed architecture and some preliminary results aimed at investigating the usage of a distributed files system as a communication media for the CAPIO middleware, with the ultimate goal of supporting both CLOUD-based and HPC-based workflows.},
	address = {Pisa, Italy},
	author = {Santimaria, Marco Edoardo and Colonnelli, Iacopo and Aldinucci, Marco},
	keywords = {icsc, capio, capiocl, across, admire, eupex},
	month = sep,
	title = {Releasing the CAPIO middleware from MPI derived constraints},
	url = {https://datacloud.di.unito.it/index.php/s/zrJGD4i36fWdp5g},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/zrJGD4i36fWdp5g}}

@misc{24:santimaria:itadata:shpcpee,
	abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to expand, further exacerbating quickly the performance gap between computing, memory, and storage technologies. CAPIO (Cross-Application Programmable I/O), is 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. In this presentation, we will introduce the CAPIO-CL language with its semantics, as well as the implementation of the CAPIO-CL language through the CAPIO middleware. We will also provide some case studies of how CAPIO has been employed to improve workflow execution time as well as some future directions.},
	address = {Pisa, Italy},
	author = {Santimaria, Marco Edoardo and Colonnelli, Iacopo and Torquati, Massimo and Aldinucci, Marco},
	keywords = {icsc, capio, capiocl, across, admire, eupex},
	month = sep,
	title = {CAPIO: Cross Application Programamble IO},
	url = {https://datacloud.di.unito.it/index.php/s/rg6LWwrZXi6tTXm},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/rg6LWwrZXi6tTXm}}

@misc{24:mittone:itadata:shpcpee,
	abstract = {Benchmarking the performance of modern High-Performance Computing (HPC) infrastructure on Artificial Intelligence (AI) workloads is a hot topic in the supercomputing community. While research communities and big-tech companies actively invest in larger, more powerful data centres to support AI research, the standard computational performance benchmarking tools (e.g., LINPACK) are increasingly becoming outdated since they are not specifically tailored for AI workloads. Some tools, such as MLPerf, are trying to bridge this gap, but the HPC community still has not adopted them as standards. Since this trend became particularly evident with the advent of Large Language Models (LLMs), this work will delve into LLM training at scale as a way to benchmark Top500 HPC infrastructures on current AI workloads. The scalability performances of a major LLM model (i.e., Meta's LLaMA) on different HPCs (Leonardo, LUMI, MeluXina, Karolina) are exposed and discussed along with their Top500 positioning. However, it should be noted that state-of-the-art LLM models are not trained on thousands of computing nodes but on hundreds. This choice is due to multiple factors, such as the influence of the training scaling on the model's convergence and the instability of large-scale deployments due to hardware/software failure. A benchmarking approach based on the next-generation LLM training approach is proposed to bypass all these issues. State-of-the-art LLMs are not monolithic structures but Mixture-of-Experts (MoE) models; this design implies innovative frontiers for the distributed training of such models due to the experts' training being potentially more parallelisable than a single monolithic model. We thus propose to create an AI-oriented HPC benchmark suite based on the parallel training of MoE models to measure the throughput performance of HPC systems on state-of-the-art AI workloads.},
	address = {Pisa, Italy},
	author = {Mittone, Gianluca},
	keywords = {icsc, eupilot, ai},
	month = sep,
	title = {Benchmarking HPC Performance for State-of-the-Art AI Workloads},
	url = {https://datacloud.di.unito.it/index.php/s/5Ep3W7cPW5baZfr},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5Ep3W7cPW5baZfr}}

@misc{23:ecmlpkdd:fedsurvboost,
	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.},
	address = {Vilnius, Lithuania},
	author = {Oussama Harrak and Bruno Casella and Samuele Fonio and Piero Fariselli and Gianluca Mittone and Cesare Rollo and Tiziana Sanavia and Marco Aldinucci},
	howpublished = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2nd Workshop on Advancements in Federated Learning (WAFL)},
	keywords = {icsc, epi, ai, fl},
	month = sep,
	title = {Federated Adaboost for Survival Analysis},
	url = {https://datacloud.di.unito.it/index.php/s/DtXiQfne6BEC235},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/DtXiQfne6BEC235}}

@misc{25: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.},
	address = {Bruges, Belgium},
	author = {Bruno Casella and Matthias Jakobs and Marco Aldinucci and Sebastian Buschjaeger},
	howpublished = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
	keywords = {icsc},
	month = oct,
	title = {Federated Time Series Classification with ROCKET features},
	url = {https://datacloud.di.unito.it/index.php/s/xWCBsMfjZMkyrAq},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/xWCBsMfjZMkyrAq}}

@misc{24:esann:fedhp,
	abstract = {This paper introduces FedHP, an innovative algorithm that integrates federated learning, hyperspherical geometries, and prototype learning. Federated Learning (FL) has gained prominence as a privacy- preserving method for building robust models across distributed datasets. Traditionally, FL exchanges model parameters to maintain data privacy; however, in scenarios with expensive data communication, exchanging large neural network models becomes impractical. In such cases, prototype learning offers a viable solution by facilitating the exchange of only a few prototypes. Motivated by these considerations, our approach capitalizes on recent advancements in prototype learning, particularly the advantages offered by non-Euclidean geometries. In addition to presenting FedHP, we offer empirical evidence demonstrating its comparability to other state-of- the-art approaches while significantly reducing communication costs.},
	address = {Bruges, Belgium},
	author = {Samuele Fonio and Mirko Polato and Roberto Esposito},
	howpublished = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN)},
	keywords = {icsc, ai, fl},
	month = oct,
	title = {FedHP: Federated Learning with Hyperspherical Prototypical Regularization},
	url = {https://datacloud.di.unito.it/index.php/s/fKyKSSFQKT3LTxW},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/fKyKSSFQKT3LTxW}}

@misc{24:mittone:TPC,
	abstract = {Big-tech companies pre-train SOTA LLMs on special-purpose, private HPCs, while public research centres lack the resources to compete. We advocate a new take on large model training, e.g., LLMs, called xFFL, which leverages federated learning as an enabling technique to exploit geographically distributed computing power to bridge such digital divide. This work introduces a proof-of-concept federated training of LLaMA-3 8B on three EuroHPC Top500 facilities, proving the viability of leveraging cross-facility publicly available computational power to sustain SOTA LLM workloads.},
	address = {Atlanta, GE, USA},
	author = {Mittone, Gianluca and Mulone, Alberto and Colonnelli, Iacopo and Birke, Robert and Aldinucci, Marco},
	howpublished = {Accelerating the Development and Use of Generative AI for Science and Engineering: The Trillion Parameter Consortium ({TPC})},
	keywords = {icsc, eupilot, space, fl, streamflow},
	month = nov,
	title = {Enabling Cross-Facility LLMs Pre-Training},
	url = {https://datacloud.di.unito.it/index.php/s/DRgm8ebBkKQgD2d},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/DRgm8ebBkKQgD2d}}

@misc{24:malenza:p3hpc,
	address = {Atlanta, US},
	author = {Malenza, Giulio},
	howpublished = {{IEEE}},
	keywords = {icsc, eupex},
	month = nov,
	title = {{Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study}},
	url = {https://datacloud.di.unito.it/index.php/s/55T6LbGcWtssNno},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/55T6LbGcWtssNno}}

@misc{24:brescia:swiss:talk,
	address = {Neuchatel, Switzerland},
	author = {Brescia, Lorenzo},
	howpublished = {{Presentation of previous works and future directions on securing workflows}},
	keywords = {icsc, confidential},
	month = nov,
	title = {{Secure workflow computation}},
	url = {https://datacloud.di.unito.it/index.php/s/5nwZ2bi7by3twQB},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5nwZ2bi7by3twQB}}

@misc{23:casella:architecturalfedavgtalk,
	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},
	keywords = {icsc, epi, ai, fl},
	title = {Architecture-Based FedAvg for Vertical Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/kJQxnqG4d2ZSicK},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/kJQxnqG4d2ZSicK}}

@misc{23:wide:medic,
	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},
	keywords = {eupex, icsc},
	title = {Towards formal model for location aware workflows},
	url = {https://datacloud.di.unito.it/index.php/s/wpDd9HETzioixTW},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/wpDd9HETzioixTW}}

@misc{23:flw:statale,
	address = {Milano, Italy},
	author = {Marco Aldinucci},
	howpublished = {Universit\`a degli Studi di Milano},
	keywords = {icsc, hpc4ai, fl},
	month = jan,
	title = {Federated Learning: A Distributed System Viewpoint},
	year = 2023}

@misc{23:ACMCF,
	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 processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us 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 x86-64 and ARM platforms and an emerging RISC-V one. 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.},
	annote = {invited},
	author = {Gianluca Mittone and Nicol{\`o} Tonci and Robert Birke and Iacopo Colonnelli and Doriana Medi{\'c} and Andrea Bartolini and Roberto Esposito and Emanuele Parisi and Francesco Beneventi and Mirko Polato and Massimo Torquati and Luca Benini and Marco Aldinucci},
	howpublished = {20th ACM international conference on computing frontiers (CF '23)},
	keywords = {eupilot, icsc, ai},
	month = may,
	note = {Invited talk},
	title = {Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning},
	url = {https://datacloud.di.unito.it/index.php/s/BYyqZbHzzN4DL8Z},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/BYyqZbHzzN4DL8Z}}

@misc{23:WWW,
	abstract = {Aggregating pharmaceutical data in the drug-target interaction (DTI) domain can potentially 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 is reconcilable with the industry's constraints. It does not require sharing any information that would reveal the entities' data or any other high-level summary. When used on a representative GraphDTA model and the KIBA dataset, it achieves up to 15\% 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.},
	annote = {invited},
	author = {Gianluca Mittone and Filip Svoboda and Marco Aldinucci and Nicholas D. Lane and Pietro Lio'},
	howpublished = {2023 ACM international Web Conference (WWW '23)},
	keywords = {eupilot, icsc},
	month = may,
	note = {Invited talk},
	title = {A Federated Learning Benchmark for Drug-Target Interaction},
	url = {https://datacloud.di.unito.it/index.php/s/js7go3EorZxSLn9},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/js7go3EorZxSLn9}}

@misc{23:mulone:wide:talk,
	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},
	howpublished = {47th {IEEE} Annual Computers, Software, and Applications Conference, {COMPSAC} 2023},
	keywords = {across, icsc, streamflow},
	month = jun,
	title = {Porting the {V}ariant {C}alling {P}ipeline for {NGS} data in cloud-{HPC} environment},
	url = {https://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy}}

@misc{23:OF:genova,
	address = {Genova, Italy},
	author = {Giulio Malenza},
	howpublished = {18th {O}pen{FOAM} Workshop 2023, {G}enova},
	keywords = {icsc},
	month = jul,
	title = {Building an accelerated OpenFOAM Proof-of-Concept application using Modern C++.},
	url = {https://datacloud.di.unito.it/index.php/s/mB6omsDB8ERBkGW},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/mB6omsDB8ERBkGW}}

@misc{23:europar:phdtalk,
	abstract = {Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). 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 processors (e.g., RISC-V), non-fully connected network topologies, and asynchronous collaboration schemes. We overcome these limitations via a domain-specific language allowing us to map DML schemes to an underlying middleware, i.e. the FastFlow parallel programming library.},
	address = {Limassol, Cyprus},
	author = {Gianluca Mittone and Robert Birke and Marco Aldinucci},
	howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},
	keywords = {eupilot, icsc},
	month = aug,
	title = {Model-Agnostic Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/pT3qxkwzzsHR3nS},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/pT3qxkwzzsHR3nS}}

@misc{23:GAIA:bigHPC,
	address = {Naples, Italy},
	author = {Malenza, Giulio and Cesare, Valentina and Aldinucci, Marco},
	howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},
	keywords = {icsc},
	month = sep,
	title = {Performance portability in {HPC}: the {G}aia use-case.},
	url = {https://datacloud.di.unito.it/index.php/s/RqcZpizFtC9toFq},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/RqcZpizFtC9toFq}}

@misc{23:iciap:benchmed,
	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 = {Samuele Fonio},
	howpublished = {Image Analysis and Processing - ICIAP 2023 - 22th International Conference - FedMed},
	keywords = {icsc, eupilot, ai, fl},
	month = sep,
	title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},
	url = {https://datacloud.di.unito.it/index.php/s/sR7YeTGgfH4DtCR},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/sR7YeTGgfH4DtCR}}

@misc{23:itadata:fl_scaling,
	abstract = {Federated Learning (FL) is a widespread Machine Learning paradigm handling distributed Big Data. In this work, we demonstrate that different FL frameworks expose different scaling performances despite adopting the same technologies, highlighting the need for a more comprehensive study on the topic.},
	address = {Naples, Italy},
	annote = {invited},
	author = {Gianluca Mittone and Samuele Fonio},
	howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},
	keywords = {eupilot, icsc, ai, fl},
	month = sep,
	title = {Benchmarking Federated Learning Scalability},
	url = {https://datacloud.di.unito.it/index.php/s/QZGxC4X3s5LG5oT},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/QZGxC4X3s5LG5oT}}

@misc{23:HaMMonProject,
	address = {Bologna, Italy},
	author = {Iacopo Colonnelli and Doriana Medi\'{c} and Barbara Cantalupo and Marco Aldinucci},
	howpublished = {HaMMon Kick-Off meeting},
	keywords = {icsc, streamflow},
	month = oct,
	title = {Universit\`{a} degli Studi di Torino: Alpha parallel research group},
	url = {https://datacloud.di.unito.it/index.php/s/cmgy9BZ3nwCR2QJ},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/cmgy9BZ3nwCR2QJ}}

@misc{23:sc:works,
	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 developing an informed view of current and future research directions. This work conducts a systematic mapping study of the Italian workflow research community, 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 = {Marco Aldinucci and Elena Baralis and Valeria Cardellini and Iacopo Colonnelli and Marco Danelutto and Sergio Decherchi and Giuseppe Di Modica and Luca Ferrucci and Marco Gribaudo and Francesco Iannone and Marco Lapegna and Doriana Medi\'{c} and Giuseppa Muscianisi and Francesca Righetti and Eva Sciacca and Nicola Tonellotto and Mauro Tortonesi and Paolo Trunfio and Tullio Vardanega},
	howpublished = {18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023)},
	keywords = {icsc},
	month = nov,
	title = {A Systematic Mapping Study of Italian Research on Workflows},
	url = {https://datacloud.di.unito.it/index.php/s/2kgooG43pGCykji},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/2kgooG43pGCykji}}

@misc{23:brescia:trusted:workflow:fl4:talk,
	address = {Turin, Italy},
	author = {Brescia, Lorenzo and Colonnelli, Iacopo},
	howpublished = {{CN HPC Flagship 4 Working Day}},
	keywords = {icsc, confidential},
	month = dec,
	title = {{Trusted Computing at Scale}},
	url = {https://datacloud.di.unito.it/index.php/s/5ij6tLd5SAX4Nn4},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5ij6tLd5SAX4Nn4}}

@misc{23:ucc: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},
	annote = {invited},
	author = {Gianluca Mittone and Giulio Malenza and Marco Aldinucci and Robert Birke},
	howpublished = {The 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023)},
	keywords = {eupilot, icsc, ai},
	month = dec,
	title = {Distributed Edge Inference: an Experimental Study on Multiview Detection},
	url = {https://datacloud.di.unito.it/index.php/s/XfjNZEPSNfSKPFr},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/XfjNZEPSNfSKPFr}}

@misc{22:cip:romania,
	abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
	address = {Bucharest, Romania},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
	month = jun,
	note = {Invited talk},
	title = {EuroHPC and the Italian HPC ecosystem},
	url = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn}}

@misc{22:eocoe:summit,
	abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
	address = {Napoli, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {EuroHPC EoCoE final summit},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
	month = jun,
	note = {Invited talk},
	title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},
	url = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB}}

@misc{22:garr,
	abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},
	address = {Palermo, Italy},
	annote = {keynote},
	author = {Marco Aldinucci},
	howpublished = {Condivisioni, Conferenza GARR 2022},
	keywords = {hpc4ai, across, eupex, across, admire, textarossa, eumaster4hpc, icsc},
	month = may,
	note = {Keynote talk},
	title = {Da {HPC4AI} al living lab dello spoke {FutureHPC} del Centro Nazionale {HPC}},
	url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc}}

@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}}

@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: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{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{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{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{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}}

@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}}

@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}}

@misc{26:europar:fldp,
	abstract = {This presentation, delivered by Gianluca Mittone and Marco Aldinucci (University of Turin) at the 32nd International European Conference on Parallel and Distributed Computing (Euro-Par 26, Pisa, August 27th, 2026), introduces FL+FSDP and FL+HSDP, hybrid schemes that accelerate sharded data parallelism at scale by integrating federated learning (FL) aggregations. The talk begins by motivating the work: foundation models (FMs) are driving an exponential growth in hardware demands, with model sizes, training datasets, and parallelism scales increasing by orders of magnitude, as shown by compute-trend analyses. It reviews the taxonomy of FM parallelization and distribution techniques, then focuses on Fully Sharded Data Parallelism (FSDP) and Hybrid Sharded Data Parallelism (HSDP), explaining how model shards are reconstructed through AllGather/ReduceScatter and how HSDP adds a replication dimension via AllReduce. The core contribution is relaxing mathematical coherency with sequential training through FL: between FL aggregations, models can diverge, but if data is IID, FL statistically converges as sequential training, with N (aggregation frequency) providing a knob to balance communication efficiency and coherency. The presentation formalizes communication costs for FSDP, HSDP, FL, FL+FSDP, and FL+HSDP, and shows how FL+HSDP models a three-tier communication hierarchy---sharding, replication, and federation groups---that maps naturally onto complex HPC network topologies like DragonFly+, exploiting different bandwidth levels (intra-node NVLink ~1.8 TB/s, inter-node InfiniBand ~100 GB/s, and inter-group links). Experimental results on Llama3.1-8B training over ~150M tokens on 512 A100 GPUs (128 nodes) of the Leonardo supercomputer show that FL+FSDP processes data up to 8.04× faster and achieves 4.48× lower evaluation perplexity than baseline sharded DP methods, without hyperparameter tuning. Key contributions include the hybrid communication scheme, interconnect-aware training, theoretical communication-cost analysis, and empirical validation. Future work includes extending FL as a fifth orthogonal parallelism axis, automated topology-aware configuration, elimination of manual tuning, and forward-recovery fault tolerance using Federation Groups as alive backups.},
	address = {Pisa, Italy},
	author = {Mittone, Gianluca and Aldinucci, Marco},
	howpublished = {32nd International European Conference on Parallel and Distributed Computing (EuroPar26)},
	keywords = {icsc, dyman, space, fl},
	month = aug,
	note = {Best Paper Award},
	title = {Accelerating sharded data parallelism at scale with federated learning},
	url = {https://datacloud.di.unito.it/index.php/s/CYHFFwStdqDt4Cj},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/CYHFFwStdqDt4Cj}}

@misc{25:cineca:xffl,
	abstract = {Big-tech companies pre-train SOTA LLMs on special-purpose, private HPCs, while public research centres lack the resources to compete. We advocate a new take on large model training, e.g., LLMs, called xFFL, which leverages federated learning as an enabling technique to exploit geographically distributed computing power to bridge such digital divide. This work introduces a proof-of-concept federated training of LLaMA-3 8B on three EuroHPC Top500 facilities, proving the viability of leveraging cross-facility publicly available computational power to sustain SOTA LLM workloads.},
	address = {Bologna, Italy},
	author = {Mittone, Gianluca and Mulone, Alberto and Colonnelli, Iacopo and Birke, Robert and Aldinucci, Marco},
	howpublished = {AI & HPC User Forums and HPC User Day},
	keywords = {icsc, eupilot, space, fl, streamflow},
	month = mar,
	title = {Cross-Facility Federated Learning},
	url = {https://datacloud.di.unito.it/index.php/s/oXoeHLytc9CZ5KR},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/oXoeHLytc9CZ5KR}}

@misc{25:riken:ftfl5,
	abstract = {This fourth periodic meeting presentation (October 28, 2025) reports on simulating full-scale LLM pre-training to study fault tolerance through FL. It introduces discrete-event and agent-based simulation, implemented with SimPy. Each GPU is modeled as an agent with lifecycle stages---Setup, Compute, Checkpoint, and Wait---organized into groups. Simulation parameters include setup time (~20 min), compute time (~1 s per sample), checkpoint interval (~10 min) and checkpoint time (~1 min), stochastic failure probability (~1e-5 per time step), and downtime (~1 h), each with 10% Gaussian variance. Preliminary results compare FSDP, FL-FR (forward recovery), and FL-FS (fail-stop) across node counts (4 GPUs per node). Metrics include overall execution time, setup time, checkpoint count, cumulative checkpointing time, failure count, and cumulative failure time. FL-FS generally reduces checkpointing overhead and failure impact at scale. Next steps include modeling setup and checkpoint failures, using real-world failure rates, simulating different convergence rates, estimating optimal checkpoint latency, and moving time steps to minutes.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: Fourth Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/mbWsNzAkmWDbdke},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/mbWsNzAkmWDbdke}}

@misc{25:riken:ftfl4,
	abstract = {This third periodic meeting presentation (October 14, 2025) presents a bottom-up approach to fault tolerance in large-scale ML training through FL. It describes the pre-training hardware/software stack---AI algorithms, frameworks, toolkits, and hardware---and how node-level complexity leads to noisy fault manifestations. It reviews failure frequency and cost, using pie charts from GPT-3 and Platform-X/Unicorn, and categorizes faults by severity (SEV1--SEV3). The talk details NCCL socket retry parameters (NCCL_SOCKET_RETRY_CNT, NCCL_SOCKET_RETRY_SLEEP_MSEC) and NCCL communicator shrinking via ncclCommShrink with NCCL_SHRINK_ABORT. It then presents xFFL deployment scenarios for a single sequential client per node and for distributed multi-node clients, showing two recovery approaches when a node crashes: shrink the NCCL communicator to exclude the failed node, or rebuild the distributed backend to exclude the client. If a node rejoins, the backend is rebuilt and other peers act as alive checkpoints. It compares PyTorch init_process_group and NCCL shrink setup times, advocating framework-handled fault tolerance with minimal user code and low impact on training performance.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: Third Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/iokaxmMcyy76B46},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/iokaxmMcyy76B46}}

@misc{25:riken:ftfl3,
	abstract = {This second periodic meeting presentation (September 30, 2025) focuses on fault tolerance for large-scale ML training through FL, with emphasis on LLM pre-training. It contrasts pre-training (huge unlabeled corpora, thousands of GPUs, weeks/months, high failure risk) and fine-tuning (small labeled datasets, few GPUs, hours/days, manageable risk). The talk presents xFFL's goal to make pre-training less communication-bound via intra-silo FedAvg, including layer-wise and bucket-wise aggregation. It analyzes failure scenarios using GPT-3 pre-training statistics on Alibaba Cloud, distinguishing transient faults (~73% of errors) from hardware faults (~27%) and their downtime costs. Current fault-tolerance approaches are reviewed: checkpointing, TorchSnapshot, SpikingBrain/DLRover Flash Checkpoint, Torchrun elasticity, pipeline-parallelism templates (Bamboo/Ooblack), and Gemini. Challenges include FL's extra redundancy, dynamic restart, zero-redundancy checkpointing, and communicator recreation. The proposed approach is framework-handled fault tolerance using NCCL low-level features---ncclCommAbort, ncclCommShrink, and socket retry parameters---to move from a fail-stop model toward retry and autonomous recovery, reserving rollback as a last resort.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: Second Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/AWPR5ASwwZt6gJ7},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/AWPR5ASwwZt6gJ7}}

@misc{25:riken:ftfl1,
	abstract = {This first periodic meeting presentation, held at RIKEN R-CCS (September 9, 2025), reviews fault tolerance in large-scale machine learning training through federated learning. It outlines the current xFFL implementation, which supports two deployment typologies: cross-facility (inter-cluster) FL using a master-worker topology coordinated by StreamFlow with built-in retry-rollback fault tolerance, and single-facility (intra-cluster) FL using peer-to-peer NCCL all-reduce, which still lacks fault tolerance. The talk discusses checkpointing and rollback strategies for centralized and decentralized FL, including server-side, client-side, individual, and collective checkpointing. It surveys fault-tolerant communication libraries---OpenMPI/MPICH with ULFM, MVAPICH2 checkpoint/restart features, and NCCL communicator abort/shrink capabilities---and covers PyVerbs for RDMA and Torchrun for elastic distributed training. Key open issues include assumptions of a reliable server, fail-stop client models, and the need for more flexible fault-tolerant communication. The presentation sets the stage for integrating fault tolerance directly into xFFL.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Fault Tolerance in Large-Scale ML Training through FL: First Periodic Meeting},
	url = {https://datacloud.di.unito.it/index.php/s/W9J2i5PnZs5DiJk},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/W9J2i5PnZs5DiJk}}

@misc{25:riken:ftfl0,
	abstract = {This kick-off presentation, delivered by Gianluca Mittone at RIKEN R-CCS (Kobe, Japan, September 2, 2025), introduces three innovative distributed intelligence approaches for pushing federated learning (FL) boundaries. After an overview of the speaker's academic and research background, the talk motivates FL through privacy regulations and the standard FedAvg algorithm. It then presents three main contributions: (1) Model-Agnostic Federated Learning (MAFL/OpenFL-x), which extends OpenFL to support traditional machine learning models via AdaBoost.F, enabling non-deep model federation; (2) High-Performance Federated Learning (FastFL), a FastFlow/RISC-pb2l-based backend supporting multiple communication topologies---master-worker, peer-to-peer, and tree---with strong and weak scaling on HPC and heterogeneous microarchitectures; and (3) Cross-Facility Federated Learning (xFFL), which uses StreamFlow to coordinate federated training across multiple HPC facilities, demonstrated with LLaMA-2/3 pre-training across Leonardo, LUMI, and MeluXina. The talk concludes with future work on RDMA-based fault tolerance and extra applications such as edge-distributed multiview detection and drug-target interaction.},
	address = {Kobe, Japan},
	author = {Mittone, Gianluca},
	howpublished = {RIKEN Center for Computational Science},
	keywords = {icsc, eupilot, space, fl},
	month = sep,
	title = {Pushing Federated Learning Boundaries: Three Innovative Distributed Intelligence Approaches},
	url = {https://datacloud.di.unito.it/index.php/s/Z9ZD2c3MPya2CS3},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/Z9ZD2c3MPya2CS3}}

@misc{24:icolonne:cwl4hpccern,
	address = {CERN, Meyrin, Switzerland},
	author = {Colonnelli, Iacopo},
	howpublished = {Workshop on workflow languages for {HEP} analysis},
	keywords = {across, eupex, space, icsc, streamflow},
	month = apr,
	title = {CWL in the HPC Ecosystem},
	url = {https://datacloud.di.unito.it/index.php/s/PRmqdwWHt6P2PH7},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/PRmqdwWHt6P2PH7}}

@misc{24:ic:elise:xffl,
	address = {Helsinki, Finland},
	author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Marco Aldinucci},
	keywords = {icsc,space,eupex},
	month = jun,
	note = {Invited talk},
	title = {Cross-Facility Federated Learning - Part II},
	url = {https://datacloud.di.unito.it/index.php/s/7HonBpcWPxotXLX},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/7HonBpcWPxotXLX}}

@misc{24:mittone:TPC,
	abstract = {Big-tech companies pre-train SOTA LLMs on special-purpose, private HPCs, while public research centres lack the resources to compete. We advocate a new take on large model training, e.g., LLMs, called xFFL, which leverages federated learning as an enabling technique to exploit geographically distributed computing power to bridge such digital divide. This work introduces a proof-of-concept federated training of LLaMA-3 8B on three EuroHPC Top500 facilities, proving the viability of leveraging cross-facility publicly available computational power to sustain SOTA LLM workloads.},
	address = {Atlanta, GE, USA},
	author = {Mittone, Gianluca and Mulone, Alberto and Colonnelli, Iacopo and Birke, Robert and Aldinucci, Marco},
	howpublished = {Accelerating the Development and Use of Generative AI for Science and Engineering: The Trillion Parameter Consortium ({TPC})},
	keywords = {icsc, eupilot, space, fl, streamflow},
	month = nov,
	title = {Enabling Cross-Facility LLMs Pre-Training},
	url = {https://datacloud.di.unito.it/index.php/s/DRgm8ebBkKQgD2d},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/DRgm8ebBkKQgD2d}}

@misc{23:HBPSummit,
	abstract = {A hands-on training offer for Standardised Workflows in EBRAINS. A short presentation will be used as an introduction, while the main hands-on session will provide information about Writing and Executing Standardised Workflows. TC will give some guidelines, so attendees can experiment with writing CWL tools and workflows and then they will be given access to VM to execute these workflows. The Workflows Dashboard will be also presented during the same session, offering to the attendees the opportunity to understand the different functionalities, use it with TC support and provide useful comments.},
	address = {Marseille, France},
	annote = {invited},
	author = {Sofia Karvounari and Eleni Mathioulaki and Michael R. Crusoe and Iacopo Colonnelli},
	howpublished = {Human Brain Project Summit 2023},
	keywords = {streamflow, across, eupex, space},
	month = mar,
	note = {Invited talk},
	title = {Standardised Workflows at {EBRAINS}},
	url = {https://datacloud.di.unito.it/index.php/s/K5YQKTsX9N7NLT8},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/K5YQKTsX9N7NLT8}}

@misc{23:gtc:fl,
	abstract = {Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We'll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.},
	author = {Marco Aldinucci},
	howpublished = {NVidia GTC 2023},
	keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, space, eumaster4hpc},
	month = mar,
	title = {Experimenting with Systems for Decentralized Machine Learning},
	url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c}}

@misc{23:CENTAI:hpc4ai,
	address = {Torino, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {CENTAI kick-off meeting},
	keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, eumaster4hpc, space, brainteaser},
	month = mar,
	title = {{HPC4AI}: The Research on {AI} beyond the public cloud},
	url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb}}

@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}}

@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}}

@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}}

@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{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}}

@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: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{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}}

@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{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}}

@misc{26:europar:fldp,
	abstract = {This presentation, delivered by Gianluca Mittone and Marco Aldinucci (University of Turin) at the 32nd International European Conference on Parallel and Distributed Computing (Euro-Par 26, Pisa, August 27th, 2026), introduces FL+FSDP and FL+HSDP, hybrid schemes that accelerate sharded data parallelism at scale by integrating federated learning (FL) aggregations. The talk begins by motivating the work: foundation models (FMs) are driving an exponential growth in hardware demands, with model sizes, training datasets, and parallelism scales increasing by orders of magnitude, as shown by compute-trend analyses. It reviews the taxonomy of FM parallelization and distribution techniques, then focuses on Fully Sharded Data Parallelism (FSDP) and Hybrid Sharded Data Parallelism (HSDP), explaining how model shards are reconstructed through AllGather/ReduceScatter and how HSDP adds a replication dimension via AllReduce. The core contribution is relaxing mathematical coherency with sequential training through FL: between FL aggregations, models can diverge, but if data is IID, FL statistically converges as sequential training, with N (aggregation frequency) providing a knob to balance communication efficiency and coherency. The presentation formalizes communication costs for FSDP, HSDP, FL, FL+FSDP, and FL+HSDP, and shows how FL+HSDP models a three-tier communication hierarchy---sharding, replication, and federation groups---that maps naturally onto complex HPC network topologies like DragonFly+, exploiting different bandwidth levels (intra-node NVLink ~1.8 TB/s, inter-node InfiniBand ~100 GB/s, and inter-group links). Experimental results on Llama3.1-8B training over ~150M tokens on 512 A100 GPUs (128 nodes) of the Leonardo supercomputer show that FL+FSDP processes data up to 8.04× faster and achieves 4.48× lower evaluation perplexity than baseline sharded DP methods, without hyperparameter tuning. Key contributions include the hybrid communication scheme, interconnect-aware training, theoretical communication-cost analysis, and empirical validation. Future work includes extending FL as a fifth orthogonal parallelism axis, automated topology-aware configuration, elimination of manual tuning, and forward-recovery fault tolerance using Federation Groups as alive backups.},
	address = {Pisa, Italy},
	author = {Mittone, Gianluca and Aldinucci, Marco},
	howpublished = {32nd International European Conference on Parallel and Distributed Computing (EuroPar26)},
	keywords = {icsc, dyman, space, fl},
	month = aug,
	note = {Best Paper Award},
	title = {Accelerating sharded data parallelism at scale with federated learning},
	url = {https://datacloud.di.unito.it/index.php/s/CYHFFwStdqDt4Cj},
	year = 2026,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/CYHFFwStdqDt4Cj}}

@misc{25:asdmsd:fl,
	abstract = {Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train models while keeping raw data local. This lecture introduces the fundamental principles of FL and examines their application to foundation models, with particular emphasis on privacy, communication efficiency, data and computational heterogeneity, and system-level constraints. FL is presented as an approach for learning across decentralized data sources without directly transferring sensitive information, using focused model updates that are aggregated by a coordinating server. Privacy can be further strengthened through techniques such as differential privacy and secure aggregation. The lecture discusses the main FL deployment settings, distinguishing cross-device learning, involving potentially millions of intermittently available clients, from cross-silo learning, where organizations are generally more stable and highly available. These settings introduce challenges related to non-IID data, heterogeneous computational resources, limited connectivity, client availability, and communication overhead. Federated optimization therefore requires careful coordination between local computation, communication rounds, privacy requirements, and system constraints. Federated Averaging (FedAvg) is presented as a common optimization approach in which locally trained model parameters are periodically aggregated to produce a shared global model. A particular focus is placed on Cross-Facility Federated Learning (xFFL), which extends FL to geographically distributed HPC infrastructures. The approach uses hierarchical orchestration through StreamFlow, HPC-adapting middleware, and containerized PyTorch deployments to address differences in hardware, software environments, and infrastructure policies. Experiments with LLaMA-2 and LLaMA-3 demonstrate the application of federated learning to large language model workloads across HPC facilities. Overall, FL for foundation models is characterized as an interdisciplinary problem combining machine learning, optimization, privacy, security, cryptography, information theory, and distributed systems. The slides emphasize that effective FL systems require co-design of optimization algorithms with privacy, security, communication, computational, and deployment constraints, while careful evaluation is necessary to distinguish simulation results from behavior in real-world federated environments.},
	address = {Trieste, Italy},
	author = {Mittone, Gianluca},
	howpublished = {ICTP Advanced School on Foundation Models for Scientific Discovery},
	keywords = {icsc, dyman, fl},
	month = jul,
	title = {Federated Learning for Foundation Models},
	url = {https://datacloud.di.unito.it/index.php/s/5gzSkSgN8nzKz9D},
	year = 2025,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5gzSkSgN8nzKz9D}}

@misc{24:gmalenza:scihpcexa,
	abstract = {In today's era of rapid technological advancement, artificial intelligence (AI) applications require large-scale, high-performance, and data-intensive computations, leading to significant energy demands. Addressing this challenge necessitates a combined approach involving both hardware and software innovations. Hardware manufacturers are developing new, efficient, and specialized solutions, with the RISC-V architecture emerging as a prominent player due to its open, extensible, and energy-efficient instruction set architecture (ISA). Simultaneously, software developers are creating new algorithms and frameworks, yet their energy efficiency often remains unclear. In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. Specifically, we examine the energy consumption of deep learning inference models across various AI frameworks. By comparing the performance of different frameworks, we aim to provide a detailed understanding of how these frameworks can optimize energy consumption on this architecture.},
	address = {Pisa, Italy},
	author = {Malenza, Giulio},
	keywords = {icsc, DYMAN, ai},
	month = sep,
	title = {Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU},
	url = {https://datacloud.di.unito.it/index.php/s/5aTdyzNB6n9CREq},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5aTdyzNB6n9CREq}}

@misc{23:gtc:fl,
	abstract = {Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We'll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.},
	author = {Marco Aldinucci},
	howpublished = {NVidia GTC 2023},
	keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, space, eumaster4hpc},
	month = mar,
	title = {Experimenting with Systems for Decentralized Machine Learning},
	url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c}}

@misc{23:CENTAI:hpc4ai,
	address = {Torino, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {CENTAI kick-off meeting},
	keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, eumaster4hpc, space, brainteaser},
	month = mar,
	title = {{HPC4AI}: The Research on {AI} beyond the public cloud},
	url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb}}

@misc{22:olimpiadi:cs,
	abstract = {Lectio Magistralis alle finali nazionali delle Olimpiadi di Informatica 2022},
	address = {Biella, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Olimpiadi di Informatica},
	keywords = {eupex, across, textarossa, admire, eupilot, eumaster4hpc},
	month = sep,
	note = {Invited talk},
	title = {Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine},
	url = {https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN}}

@misc{22:soiel:ai,
	abstract = {Innanzitutto, le infrastrutture HPC stanno adottando le GPU per il loro rapporto prestazioni per watt superiore rispetto ai multicore generici. In secondo luogo, i flussi di lavoro scientifici di prossima generazione stanno integrando passaggi basati sull'intelligenza artificiale per la loro precisione nell'approssimazione e nell'analisi di fenomeni complessi. In terzo luogo, l'IA e in particolare il Machine Learning (ML) rappresentano un carico di lavoro perfetto per le GPU in termini di prestazioni e tempo di sviluppo. Oggi non possiamo ancora chiudere il cerchio eseguendo senza problemi carichi di lavoro scientifici abilitati all'intelligenza artificiale nelle infrastrutture HPC perch{\'e} il loro software di sistema e gli strumenti di sviluppo non sono progettati per i carichi di lavoro moderni, come i framework ML progettati per il cloud. {\`E} probabile che la convergenza HPC-cloud colmi il divario. Nel talk verranno presentate le infrastrutture e gli strumenti sviluppati all'Universit{\`a} di Torino per la convergenza HPC-cloud (es. HPC4AI, StreamFlow, CAPIO, Jupyter-workflow) e come sono stati utilizzati per le applicazioni di intelligenza artificiale, come la diagnosi spiegabile di polmonite COVID-19 e la tutela della privacy AI. L'esperienza maturata nella progettazione e gestione di HPC4AI costituisce il cuore della progettazione del laboratorio di contaminazione del "FutureHPC" di Torino secondo il Centro Nazionale "HPC, BigData e Quantum Computing" finanziato dal PNRR con 320M€ che dovrebbe essere operativo dal 1 settembre 2022. L'obiettivo finale del laboratorio di contaminazione {\`e} sviluppare relazioni e collaborazioni tra industria e universit{\`a}.},
	address = {Virtual event},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Intelligenza Artificiale e Business Applications},
	keywords = {eupex, across, textarossa, admire, eupilot, eumaster4hpc},
	month = sep,
	note = {Invited talk},
	title = {La convergenza HPC-cloud {\`e} l'anello mancante tra il calcolo scientifico e l'IA applicata},
	url = {https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9}}

@misc{22:itwshpc,
	address = {Torino, Italy},
	author = {Iacopo Colonnelli and Barbara Cantalupo and Doriana Medi\'{c} and Marco Aldinucci},
	howpublished = {3rd Italian Workshop on HPC (ITWSHPC)},
	keywords = {eupex, across, admire, eupilot, textarossa, eumaster4hpc},
	month = sep,
	title = {Hybrid workflows for heterogeneous distributed computing},
	url = {https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE}}

@misc{22:nvidia_hpc_roundtable,
	address = {Casalecchio di Reno, Italy},
	annote = {invited},
	author = {Iacopo Colonnelli and Marco Aldinucci},
	howpublished = {NVIDIA HPC Roundtable},
	keywords = {eupex, across, admire, eupilot, textarossa, eumaster4hpc},
	month = sep,
	note = {Invited talk},
	title = {{CINI HPC-KTT}: {HPC} {K}ey {T}echnologies and {T}ools National Lab},
	url = {https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f}}

@misc{22:hipeacvision:fl,
	abstract = {Cognitive continuum: a game theoretical approach, (maybe) data operations are too basic: read, write, copy,  remove {\ldots} The talk is aimed to contribute to the forthcoming HiPEAC Vision document},
	address = {Brussels, Belgium},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {HiPEAC Vision meeting, Brussels, 16 May 2022},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, brainteaser},
	month = may,
	title = {Cognitive continuum: a game theoretical approach},
	url = {https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E}}

@misc{22:cip:romania,
	abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
	address = {Bucharest, Romania},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
	month = jun,
	note = {Invited talk},
	title = {EuroHPC and the Italian HPC ecosystem},
	url = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn}}

@misc{22:eocoe:summit,
	abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
	address = {Napoli, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {EuroHPC EoCoE final summit},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
	month = jun,
	note = {Invited talk},
	title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},
	url = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB}}

@misc{22:garr,
	abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},
	address = {Palermo, Italy},
	annote = {keynote},
	author = {Marco Aldinucci},
	howpublished = {Condivisioni, Conferenza GARR 2022},
	keywords = {hpc4ai, across, eupex, across, admire, textarossa, eumaster4hpc, icsc},
	month = may,
	note = {Keynote talk},
	title = {Da {HPC4AI} al living lab dello spoke {FutureHPC} del Centro Nazionale {HPC}},
	url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc}}

@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}}

@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}}

@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{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: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}}

@misc{24:ASF:WINTER,
	abstract = {Generative models have achieved unprecedented quality levels across a wide range of data types. This advance often stems from the ever increasing data and compute used to train larger and larger models. One major use case of such synthetic data is in privacy-compliant data sharing. Gartner predicts that synthetic data will reduce by 2025 the need for real data by 70% for analytics and machine learning. We will look at generative models, with a special focus on tabular data, and the issue of democratization of large model training.},
	address = {Le Pleynet, France},
	annote = {keynote},
	author = {Robert Birke},
	howpublished = {8th GDR RSD / ASF Winter School on Distributed Systems & Networks 2024},
	keywords = {eupilot, textarossa, ai},
	month = jan,
	note = {Keynote talk},
	title = {The impact of the advances in generative models on applications and systems},
	url = {https://datacloud.di.unito.it/index.php/s/QYTCMfWp4sY5qx4},
	year = 2024,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/QYTCMfWp4sY5qx4}}

@misc{23:Dell:hpc4ai,
	address = {Bologna, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Dell Advanced Computing Workshop 2023: HPC and Beyond},
	keywords = {hpc4ai, eupex, textarossa, admire, eupilot},
	month = feb,
	title = {From {HPC4AI} to {ICSC} living lab: Where systems are the research},
	url = {https://datacloud.di.unito.it/index.php/s/M5QRJyDxyxokcfL},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/M5QRJyDxyxokcfL}}

@misc{23:gtc:fl,
	abstract = {Decentralized machine learning (DML) enables collaborative machine learning without centralized input data. Federated learning (FL) and edge inference (EI) are examples of DML. Collaboration naturally happens at the edge of a distributed system with inherently distributed data. While tools for DML are starting to flourish, much needs to be done to get more flexible and portable tools to experiment with novel techniques, non-fully connected topologies, multiple data domains, and asynchronous collaboration schemes. We'll present recent advances in DML, aiming to improve usability in data centers and, at the edge, to widen the class of models extending FL to non-DNN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy though generative adversarial networks. Prerequisites: Intermediate understanding of machine learning methods and distributed & parallel computing.},
	author = {Marco Aldinucci},
	howpublished = {NVidia GTC 2023},
	keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, space, eumaster4hpc},
	month = mar,
	title = {Experimenting with Systems for Decentralized Machine Learning},
	url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c}}

@misc{23:CENTAI:hpc4ai,
	address = {Torino, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {CENTAI kick-off meeting},
	keywords = {hpc4ai, eupex, across, textarossa, admire, eupilot, epi, eumaster4hpc, space, brainteaser},
	month = mar,
	title = {{HPC4AI}: The Research on {AI} beyond the public cloud},
	url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb},
	year = 2023,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb}}

@misc{22:olimpiadi:cs,
	abstract = {Lectio Magistralis alle finali nazionali delle Olimpiadi di Informatica 2022},
	address = {Biella, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Olimpiadi di Informatica},
	keywords = {eupex, across, textarossa, admire, eupilot, eumaster4hpc},
	month = sep,
	note = {Invited talk},
	title = {Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine},
	url = {https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN}}

@misc{22:soiel:ai,
	abstract = {Innanzitutto, le infrastrutture HPC stanno adottando le GPU per il loro rapporto prestazioni per watt superiore rispetto ai multicore generici. In secondo luogo, i flussi di lavoro scientifici di prossima generazione stanno integrando passaggi basati sull'intelligenza artificiale per la loro precisione nell'approssimazione e nell'analisi di fenomeni complessi. In terzo luogo, l'IA e in particolare il Machine Learning (ML) rappresentano un carico di lavoro perfetto per le GPU in termini di prestazioni e tempo di sviluppo. Oggi non possiamo ancora chiudere il cerchio eseguendo senza problemi carichi di lavoro scientifici abilitati all'intelligenza artificiale nelle infrastrutture HPC perch{\'e} il loro software di sistema e gli strumenti di sviluppo non sono progettati per i carichi di lavoro moderni, come i framework ML progettati per il cloud. {\`E} probabile che la convergenza HPC-cloud colmi il divario. Nel talk verranno presentate le infrastrutture e gli strumenti sviluppati all'Universit{\`a} di Torino per la convergenza HPC-cloud (es. HPC4AI, StreamFlow, CAPIO, Jupyter-workflow) e come sono stati utilizzati per le applicazioni di intelligenza artificiale, come la diagnosi spiegabile di polmonite COVID-19 e la tutela della privacy AI. L'esperienza maturata nella progettazione e gestione di HPC4AI costituisce il cuore della progettazione del laboratorio di contaminazione del "FutureHPC" di Torino secondo il Centro Nazionale "HPC, BigData e Quantum Computing" finanziato dal PNRR con 320M€ che dovrebbe essere operativo dal 1 settembre 2022. L'obiettivo finale del laboratorio di contaminazione {\`e} sviluppare relazioni e collaborazioni tra industria e universit{\`a}.},
	address = {Virtual event},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Intelligenza Artificiale e Business Applications},
	keywords = {eupex, across, textarossa, admire, eupilot, eumaster4hpc},
	month = sep,
	note = {Invited talk},
	title = {La convergenza HPC-cloud {\`e} l'anello mancante tra il calcolo scientifico e l'IA applicata},
	url = {https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9}}

@misc{22:eage,
	abstract = {Large-scale scientific applications are facing an irreversible 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 non-functional 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},
	howpublished = {6th EAGE High Performance Computing Workshop},
	keywords = {eupex, across, textarossa, jupyter-workflow},
	month = sep,
	title = {Hybrid Workflows For Large-Scale Scientific Applications},
	url = {https://datacloud.di.unito.it/index.php/s/GScPS5LCPdt6Yoo},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/GScPS5LCPdt6Yoo}}

@misc{22:itwshpc,
	address = {Torino, Italy},
	author = {Iacopo Colonnelli and Barbara Cantalupo and Doriana Medi\'{c} and Marco Aldinucci},
	howpublished = {3rd Italian Workshop on HPC (ITWSHPC)},
	keywords = {eupex, across, admire, eupilot, textarossa, eumaster4hpc},
	month = sep,
	title = {Hybrid workflows for heterogeneous distributed computing},
	url = {https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE}}

@misc{22:nvidia_hpc_roundtable,
	address = {Casalecchio di Reno, Italy},
	annote = {invited},
	author = {Iacopo Colonnelli and Marco Aldinucci},
	howpublished = {NVIDIA HPC Roundtable},
	keywords = {eupex, across, admire, eupilot, textarossa, eumaster4hpc},
	month = sep,
	note = {Invited talk},
	title = {{CINI HPC-KTT}: {HPC} {K}ey {T}echnologies and {T}ools National Lab},
	url = {https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f}}

@misc{22:hipeacvision:fl,
	abstract = {Cognitive continuum: a game theoretical approach, (maybe) data operations are too basic: read, write, copy,  remove {\ldots} The talk is aimed to contribute to the forthcoming HiPEAC Vision document},
	address = {Brussels, Belgium},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {HiPEAC Vision meeting, Brussels, 16 May 2022},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, brainteaser},
	month = may,
	title = {Cognitive continuum: a game theoretical approach},
	url = {https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E}}

@misc{22:cip:romania,
	abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
	address = {Bucharest, Romania},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
	month = jun,
	note = {Invited talk},
	title = {EuroHPC and the Italian HPC ecosystem},
	url = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn}}

@misc{22:eocoe:summit,
	abstract = {The talk presents the main investments currently ongoing in Italy in the HPC area as well as the activity of Italian stakeholders within EuroHPC. The novel Italian National Centre on HPC (ICSC) is introduced.},
	address = {Napoli, Italy},
	annote = {invited},
	author = {Marco Aldinucci},
	howpublished = {EuroHPC EoCoE final summit},
	keywords = {admire, eupex, across, eupilot, textarossa, eumaster4hpc, icsc},
	month = jun,
	note = {Invited talk},
	title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},
	url = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB}}

@misc{22:healthycloud-workshop,
	address = {Virtual event},
	annote = {invited},
	author = {Iacopo Colonnelli},
	howpublished = {2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses},
	keywords = {streamflow, deephealth, across, eupex, textarossa},
	month = jul,
	note = {Invited talk},
	title = {{StreamFlow}},
	url = {https://datacloud.di.unito.it/index.php/s/Taz8qtzmkmn9ffT},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/Taz8qtzmkmn9ffT}}

@misc{22:textarossa-ga-meeting,
	address = {Roma, Italy},
	author = {Iacopo Colonnelli and Marco Aldinucci},
	howpublished = {TEXTAROSSA General Meeting},
	keywords = {textarossa},
	month = jun,
	title = {T4.1: Streaming models},
	url = {https://datacloud.di.unito.it/index.php/s/cNBnwSnTc8GiCkN},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/cNBnwSnTc8GiCkN}}

@misc{22:elixir-streamflow,
	address = {Virtual event},
	annote = {invited},
	author = {Iacopo Colonnelli},
	howpublished = {ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls},
	keywords = {streamflow, deephealth, across, eupex, textarossa},
	month = jun,
	note = {Invited talk},
	title = {{StreamFlow}: a topology-aware {WMS}},
	url = {https://datacloud.di.unito.it/index.php/s/Z9GsKnRCxmBdMd3},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/Z9GsKnRCxmBdMd3}}

@misc{22:garr,
	abstract = {HPC4AI is an open-access laboratory of the University of Turin open to researchers, students and companies that manages a double pair of systems: a production cloud-HPC system and its twin dedicated to development. The cloud-HPC system is implemented thanks to an extended version of the GARR cloud (OpenStack) and the SLURM workload manager. HPC4AI is specifically designed to support system software development and cloud-HPC convergence tools. Among these streamflow (WMS), jupyter-as-a-service (SaaS), portable-secure-tenant (PasS). The experience gained in the design and management of HPC4AI forms the heart of the design of the livinglab of the Turin "FutureHPC" spoke of the National Center "HPC, BigData and Quantum Computing" funded by the PNRR which should be operational from September 2022.},
	address = {Palermo, Italy},
	annote = {keynote},
	author = {Marco Aldinucci},
	howpublished = {Condivisioni, Conferenza GARR 2022},
	keywords = {hpc4ai, across, eupex, across, admire, textarossa, eumaster4hpc, icsc},
	month = may,
	note = {Keynote talk},
	title = {Da {HPC4AI} al living lab dello spoke {FutureHPC} del Centro Nazionale {HPC}},
	url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc},
	year = {2022},
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc}}

@misc{21:CINI_acm_CF_talk,
	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 = {Marco Aldinucci},
	booktitle = {{ACM Computing Frontiers}},
	keywords = {across, admire, textarossa, eupex, eupilot},
	month = may,
	publisher = {{ACM}},
	title = {The {Italian} research on {HPC} key technologies across {EuroHPC}},
	url = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k},
	year = 2021,
	bdsk-url-1 = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k}}

@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}}

@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}}

@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}}

@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{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{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.},
}

@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},
}

@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: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{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: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: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}}

@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{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{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}}

@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}}

@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{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_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}}

@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}}

@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}}

@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}}

@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: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}}

@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{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}}

@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}}

@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}}

@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: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}}