Talks | Parallel Computing

@misc{24:icolonne:cwl4hpccern,

title = {CWL in the HPC Ecosystem},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/PRmqdwWHt6P2PH7},

year  = {2024},

date = {2024-04-01},

address = {CERN, Meyrin, Switzerland},

howpublished = {Workshop on workflow languages for HEP analysis},

keywords = {across, eupex, icsc, space, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:pdp:karmarkar,

title = {Benchmarking Parallelization Models through Karmarkar`s algorithm},

author = {Malenza Giulio and Santimaria Marco Edoardo},

editor = {Horacio González-Vélez Adriana E. Chis},

url = {https://datacloud.di.unito.it/index.php/s/JjKcAJpYS7ctX9r},

doi = {10.1109/PDP62718.2024.00010},

year  = {2024},

date = {2024-03-01},

booktitle = {2024 32nd Euromicro International Conference on Parallel, Distributed and Network-based Processing},

pages = {1–8},

publisher = {IEEE},

address = {Dublin, Irelans},

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},

keywords = {HPC, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:icsc:spoke1:ifab,

title = {FLaaS: Federated Learning as a Service},

author = {Robert Birke},

url = {https://datacloud.di.unito.it/index.php/s/yHXdTnC8xEqoJ6Y},

year  = {2024},

date = {2024-02-01},

address = {Torino, Italy},

abstract = {Presentation about the Innovation Grant in collaboration with IFAB},

howpublished = {ICSC - Spoke 1 meeting},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:icsc:spoke1:eni,

title = {Cross-Platform Full Waveform Inversion},

author = {Alberto Mulone},

url = {https://datacloud.di.unito.it/index.php/s/Dd52GmaDX9pZfeC},

year  = {2024},

date = {2024-02-01},

address = {Torino, Italy},

abstract = {Presentation about the Innovation Grant in collaboration with ENI},

howpublished = {ICSC - Spoke 1 meeting},

keywords = {icsc, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:ASF:WINTER,

title = {The impact of the advances in generative models on applications and systems},

author = {Robert Birke},

url = {https://datacloud.di.unito.it/index.php/s/QYTCMfWp4sY5qx4},

year  = {2024},

date = {2024-01-01},

address = {Le Pleynet, France},

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.},

howpublished = {8th GDR RSD / ASF Winter School on Distributed Systems & Networks 2024},

note = {Keynote talk},

keywords = {ai, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{24:HiPEAC:riscv,

title = {RISC-V for AI},

author = {Gianluca Mittone},

url = {https://datacloud.di.unito.it/index.php/s/rFtxT7zryoKNGbP},

year  = {2024},

date = {2024-01-01},

address = {Garching bei München, München, Germany},

abstract = {AI-focused RISC-V-based hardware accelerators},

howpublished = {High Performance, Edge And Cloud computing Conference 2024 (HiPEAC 2024)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ADMIRE:UG-SHA,

title = {The Software Heritage Analytics Framework},

author = {Barbara Cantalupo},

url = {https://datacloud.di.unito.it/index.php/s/NbNDPaGZkDc3KyN},

year  = {2023},

date = {2023-12-01},

address = {Barcelona Supercomputing Centre, Barcelona, Spain},

abstract = {A framework for analytics on top of Software Heritage},

howpublished = {Admire User Meeting, 2023},

keywords = {admire},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ADMIRE:UG-GN,

title = {GreatNector Modelling Tool},

author = {Simone Pernice},

url = {https://datacloud.di.unito.it/index.php/s/NbNDPaGZkDc3KyN},

year  = {2023},

date = {2023-12-01},

address = {Barcelona Supercomputing Centre, Barcelona, Spain},

abstract = {A tool for system modelling I/O queue behaviour},

howpublished = {Admire User Meeting, 2023},

keywords = {admire},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:FL:bicocca,

title = {Federated Learning: A Distributed System Viewpoint},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/FfEzADQtC73GgLs},

year  = {2023},

date = {2023-12-01},

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-DDN paradigms, to improve the accuracy of models controlling normalization and frequency of communications, and to boost data privacy through generative adversarial networks.},

howpublished = {Bicocca University seminars, Milan, Italy},

note = {Invited talk},

keywords = {eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:eurohpc,

title = {Cross-Facility Federated Learning},

author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Marco Aldinucci and Valerio Basile and Marco Antonio Stranisci and Viviana Patti and Jeroen Galjaard and Lydia Y. Chen and Sanzio Bassini and Massimiliano Guarrasi and Gabriella Scipione and Jan Martinovič and Vit Vondrák},

url = {https://datacloud.di.unito.it/index.php/s/DDAz4QkJP3WZ68M},

year  = {2023},

date = {2023-12-01},

address = {Bruxelles, Belgium},

howpublished = {1st EuroHPC User Day},

keywords = {across, eupex, eupilot},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ucc:multiview,

title = {Distributed Edge Inference: an Experimental Study on Multiview Detection},

author = {Gianluca Mittone and Giulio Malenza and Marco Aldinucci and Robert Birke},

url = {https://datacloud.di.unito.it/index.php/s/XfjNZEPSNfSKPFr},

year  = {2023},

date = {2023-12-01},

address = {Taormina, Italy},

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.},

howpublished = {The 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:sc:WCIBoF,

title = {Orchestrating Multi-Domain Workflows: The ACROSS Approach},

author = {Alberto Scionti and Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/rJXcDBK4mLmS8yz},

year  = {2023},

date = {2023-11-01},

address = {Denver, CO, Usa},

howpublished = {Workflows Community: Modern Workflows for Continuum and Cross-Facility Computing},

keywords = {across, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:sc:works,

title = {A Systematic Mapping Study of Italian Research on Workflows},

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 Medić and Giuseppa Muscianisi and Francesca Righetti and Eva Sciacca and Nicola Tonellotto and Mauro Tortonesi and Paolo Trunfio and Tullio Vardanega},

url = {https://datacloud.di.unito.it/index.php/s/2kgooG43pGCykji},

year  = {2023},

date = {2023-11-01},

address = {Denver, CO, Usa},

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.},

howpublished = {18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023)},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:AssembleaHPC-KTT,

title = {ACROSS: HPC Big Data Artificial Intelligence Cross Stack Platform Towards Exascale},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/aK7es8BgFeWorjD},

year  = {2023},

date = {2023-10-01},

address = {Pisa, Italy},

howpublished = {LN HPC-KTT Assemblea Nazionale 2023},

keywords = {across, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:HaMMonProject,

title = {Università degli Studi di Torino: Alpha parallel research group},

author = {Iacopo Colonnelli and Doriana Medić and Barbara Cantalupo and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/cmgy9BZ3nwCR2QJ},

year  = {2023},

date = {2023-10-01},

address = {Bologna, Italy},

howpublished = {HaMMon Kick-Off meeting},

keywords = {icsc, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:GAIA:bigHPC,

title = {Performance portability in HPC: the Gaia use-case.},

author = {Giulio Malenza and Valentina Cesare and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/RqcZpizFtC9toFq},

year  = {2023},

date = {2023-09-01},

address = {Naples, Italy},

howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ITADATABestPhDThesis,

title = {Workflow models for heterogeneous distributed systems},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/6RqcaJ4djqFNDC8},

year  = {2023},

date = {2023-09-01},

address = {Napoli, Italy},

howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},

note = {Best PhD Thesis Award},

keywords = {jupyter-workflow, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:iciap:benchmed,

title = {Benchmarking Federated Learning Frameworks for Medical Imaging Tasks},

author = {Samuele Fonio},

url = {https://datacloud.di.unito.it/index.php/s/sR7YeTGgfH4DtCR},

year  = {2023},

date = {2023-09-01},

address = {Udine, Italy},

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.},

howpublished = {Image Analysis and Processing - ICIAP 2023 - 22th International Conference - FedMed},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:itadata:fl_scaling,

title = {Benchmarking Federated Learning Scalability},

author = {Gianluca Mittone and Samuele Fonio},

url = {https://datacloud.di.unito.it/index.php/s/QZGxC4X3s5LG5oT},

year  = {2023},

date = {2023-09-01},

address = {Naples, Italy},

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.},

howpublished = {2nd Italian Conference on Big Data and Data Science (ITADATA 2023)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:europar:mafl,

title = {Model-Agnostic Federated Learning},

author = {Gianluca Mittone and Walter Riviera and Iacopo Colonnelli and Robert Birke and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/9T6G2tRreRomBAE},

year  = {2023},

date = {2023-09-01},

address = {Limassol, Cyprus},

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). MAFL merges 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 machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5x speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.},

howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:europar:phdtalk,

title = {Model-Agnostic Federated Learning},

author = {Gianluca Mittone and Robert Birke and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/pT3qxkwzzsHR3nS},

year  = {2023},

date = {2023-08-01},

address = {Limassol, Cyprus},

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.},

howpublished = {29th International European Conference on Parallel and Distributed Computing (Euro-Par '23)},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:OF:genova,

title = {Building an accelerated OpenFOAM Proof-of-Concept application using Modern C++.},

author = {Giulio Malenza},

url = {https://datacloud.di.unito.it/index.php/s/mB6omsDB8ERBkGW},

year  = {2023},

date = {2023-07-01},

address = {Genova, Italy},

howpublished = {18th OpenFOAM Workshop 2023, Genova},

keywords = {icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:openscience:cuap,

title = {OpenScience@UNITO: Milestone 1: a novel continuous training course for data stewards},

author = {Marco Aldinucci and Rossana Damiano},

url = {https://datacloud.di.unito.it/index.php/s/r7QgMWTRGF8b2yq},

year  = {2023},

date = {2023-07-01},

address = {Torino, Italy},

howpublished = {University of Torino research commission},

note = {OpenScience group of the University of Torino},

keywords = {openscience},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:mulone:wide:vcp,

title = {Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment},

author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},

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},

url = {https://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy},

doi = {10.1109/COMPSAC57700.2023.00288},

year  = {2023},

date = {2023-06-01},

booktitle = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},

pages = {1858–1863},

publisher = {IEEE},

address = {Torino, Italy},

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.},

howpublished = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},

keywords = {across, icsc, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:SA2Micro,

title = {Workflows and the Common Workflow Language (CWL)},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/NHWWzMMaQgAsA52},

year  = {2023},

date = {2023-05-01},

address = {Torino, Italy},

howpublished = {OSA2Micro: An Open Science Approach to Microbiology data integration},

note = {Invited talk},

keywords = {streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:FL3WorkingDay,

title = {UNITO tools presentation},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/fgHbnLDQSFtcwLd},

year  = {2023},

date = {2023-05-01},

address = {Bologna, Italy},

howpublished = {CN HPC Flagship 3 Working Day},

keywords = {jupyter-workflow, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:ACMCF,

title = {Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning},

author = {Gianluca Mittone and Nicolò Tonci and Robert Birke and Iacopo Colonnelli and Doriana Medić and Andrea Bartolini and Roberto Esposito and Emanuele Parisi and Francesco Beneventi and Mirko Polato and Massimo Torquati and Luca Benini and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/BYyqZbHzzN4DL8Z},

year  = {2023},

date = {2023-05-01},

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.},

howpublished = {20th ACM international conference on computing frontiers (CF '23)},

note = {Invited talk},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:WWW,

title = {A Federated Learning Benchmark for Drug-Target Interaction},

author = {Gianluca Mittone and Filip Svoboda and Marco Aldinucci and Nicholas D. Lane and Pietro Lio'},

url = {https://datacloud.di.unito.it/index.php/s/js7go3EorZxSLn9},

year  = {2023},

date = {2023-05-01},

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.},

howpublished = {2023 ACM international Web Conference (WWW '23)},

note = {Invited talk},

keywords = {eupilot, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:HBPSummit,

title = {Standardised Workflows at EBRAINS},

author = {Sofia Karvounari and Eleni Mathioulaki and Michael R. Crusoe and Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/K5YQKTsX9N7NLT8},

year  = {2023},

date = {2023-03-01},

address = {Marseille, France},

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.},

howpublished = {Human Brain Project Summit 2023},

note = {Invited talk},

keywords = {across, eupex, space, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:gtc:fl,

title = {Experimenting with Systems for Decentralized Machine Learning},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/oyLt7xwkbKxz65c},

year  = {2023},

date = {2023-03-01},

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.},

howpublished = {NVidia GTC 2023},

keywords = {across, admire, epi, eumaster4hpc, eupex, eupilot, hpc4ai, space, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:CENTAI:hpc4ai,

title = {HPC4AI: The Research on AI beyond the public cloud},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/PZXjPm8sfKTmTGb},

year  = {2023},

date = {2023-03-01},

address = {Torino, Italy},

howpublished = {CENTAI kick-off meeting},

keywords = {across, admire, brainteaser, epi, eumaster4hpc, eupex, eupilot, hpc4ai, space, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:CWLConference,

title = {CWL for HPC: are we there yet?},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/CMCd5LiZeXsxwEg},

year  = {2023},

date = {2023-03-01},

address = {Heidelberg, Germany},

abstract = {Modern HPC applications are becoming so heterogeneous and complex that a modular approach to their design, deployment and orchestration is now necessary. This talk explores the benefits of using a vendor-agnostic workflow language (CWL) coupled with a hybrid workflow management system (StreamFlow) in the HPC ecosystem. Also, it will examine the requirements needed to model HPC applications effectively, the CWL’s readiness to meet such requirements, and the proposals made to improve the language where needed. Four real use cases will drive the discussion: the ACROSS Project (G.A. n. 955648), where CWL is the primary interface to model three HPC workflows, and the EUPEX Project (G.A. n. 101033975), where StreamFlow will be used for the rapid prototyping of a seismic engineering HPC application for a Modular Supercomputing Architecture (MSA) system.},

howpublished = {2023 CWL Conference},

note = {Invited talk},

keywords = {across, eupex, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:Dell:hpc4ai,

title = {From HPC4AI to ICSC living lab: Where systems are the research},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/M5QRJyDxyxokcfL},

year  = {2023},

date = {2023-02-01},

address = {Bologna, Italy},

howpublished = {Dell Advanced Computing Workshop 2023: HPC and Beyond},

keywords = {admire, eupex, eupilot, hpc4ai, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:hipeac,

title = {Paving the way to innovative tools for Federated Learning},

author = {Gianluca Mittone},

url = {https://datacloud.di.unito.it/index.php/s/2GtxPidHq79RTzA},

year  = {2023},

date = {2023-02-01},

address = {Toulouse, France},

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. Furthermore, 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 ff parallel programming library. As a byproduct, we introduce a RISC-V porting of the PyTorch framework, the first publicly available to our knowledge.},

howpublished = {2023 HiPEAC Conference},

note = {Invited talk},

keywords = {eupilot},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:wide:medic,

title = {Towards formal model for location aware workflows},

author = {Doriana Medić and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/wpDd9HETzioixTW},

year  = {2023},

date = {2023-01-01},

address = {Torino, Italy},

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 π-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, π-calculus, could be a good candidate to represent the behavioural perspective of the workflow system.},

keywords = {eupex, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{23:casella:architecturalfedavg,

title = {Architecture-Based FedAvg for Vertical Federated Learning},

author = {Bruno Casella and Samuele Fonio},

url = {https://datacloud.di.unito.it/index.php/s/kJQxnqG4d2ZSicK},

doi = {10.1109/ICCVW60793.2023.00362},

year  = {2023},

date = {2023-01-01},

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},

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.},

note = {https://iris.unito.it/bitstream/2318/1949730/1/HALF_HVL_for_DML_ICC23___Taormina-2.pdf},

keywords = {ai, epi, fl, icsc},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:itwshpc,

title = {Hybrid workflows for heterogeneous distributed computing},

author = {Iacopo Colonnelli and Barbara Cantalupo and Doriana Medić and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ienbcA2DJ26aioE},

year  = {2022},

date = {2022-09-01},

address = {Torino, Italy},

howpublished = {3rd Italian Workshop on HPC (ITWSHPC)},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:itadata,

title = {Benchmarking FedAvg and FedCurv for Image Classification Tasks},

author = {Bruno Casella},

url = {https://datacloud.di.unito.it/index.php/s/6XaEXnAowRrAHGL},

year  = {2022},

date = {2022-09-01},

address = {Milan, Italy},

abstract = {Presentation of the paper "Benchmarking FedAvg and FedCurv for Image Classification Tasks" to the first italian conference on Big Data and Data Science},

howpublished = {ITADATA},

keywords = {eupilot},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:olimpiadi:cs,

title = {Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/7ZdfLkn3NetzXCN},

year  = {2022},

date = {2022-09-01},

address = {Biella, Italy},

abstract = {Lectio Magistralis alle finali nazionali delle Olimpiadi di Informatica 2022},

howpublished = {Olimpiadi di Informatica},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:soiel:ai,

title = {La convergenza HPC-cloud è l'anello mancante tra il calcolo scientifico e l'IA applicata},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/xCQSqJ8bCKCXMK9},

year  = {2022},

date = {2022-09-01},

address = {Virtual event},

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 perché 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. È probabile che la convergenza HPC-cloud colmi il divario. Nel talk verranno presentate le infrastrutture e gli strumenti sviluppati all'Università 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 è sviluppare relazioni e collaborazioni tra industria e università.},

howpublished = {Intelligenza Artificiale e Business Applications},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:eage,

title = {Hybrid Workflows For Large-Scale Scientific Applications},

author = {Iacopo Colonnelli and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/GScPS5LCPdt6Yoo},

year  = {2022},

date = {2022-09-01},

address = {Milano, Italy},

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.},

howpublished = {6th EAGE High Performance Computing Workshop},

keywords = {across, eupex, jupyter-workflow, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:nvidia_hpc_roundtable,

title = {CINI HPC-KTT: HPC Key Technologies and Tools National Lab},

author = {Iacopo Colonnelli and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/9EQniZ2dGzdJ26f},

year  = {2022},

date = {2022-09-01},

address = {Casalecchio di Reno, Italy},

howpublished = {NVIDIA HPC Roundtable},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:data-on-kubernetes,

title = {Dossier: multi-tenant distributed Jupyter Notebooks},

author = {Iacopo Colonnelli and Dario Tranchitella},

url = {https://datacloud.di.unito.it/index.php/s/RNqTGmTqWS66qHT},

year  = {2022},

date = {2022-07-01},

address = {Virtual event},

abstract = {When providing data analysis as a service, one must tackle several problems. Data privacy and protection by design are crucial when working on sensitive data. Performance and scalability are fundamental for compute-intensive workloads, e.g. training Deep Neural Networks. User-friendly interfaces and fast prototyping tools are essential to allow domain experts to experiment with new techniques. Portability and reproducibility are necessary to assess the actual value of results. Kubernetes is the best platform to provide reliable, elastic, and maintainable services. However, Kubernetes alone is not enough to achieve large-scale multi-tenant reproducible data analysis. OOTB support for multi-tenancy is too rough, with only two levels of segregation (i.e. the single namespace or the entire cluster). Offloading computation to off-cluster resources is non-trivial and requires the user's manual configuration. Also, Jupyter Notebooks per se cannot provide much scalability (they execute locally and sequentially) and reproducibility (users can run cells in any order and any number of times). The Dossier platform allows system administrators to manage multi-tenant distributed Jupyter Notebooks at the cluster level in the Kubernetes way, i.e. through CRDs. Namespaces are aggregated in Tenants, and all security and accountability aspects are managed at that level. Each Notebook spawns into a user-dedicated namespace, subject to all Tenant-level constraints. Users can rely on provisioned resources, either in-cluster worker nodes or external resources like HPC facilities. Plus, they can plug their computing nodes in a BYOD fashion. Notebooks are interpreted as distributed workflows, where each cell is a task that one can offload to a different location in charge of its execution.},

howpublished = {DoK Talks 141},

note = {Invited talk},

keywords = {across, deephealth, hpc4ai, jupyter-workflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:healthycloud-workshop,

title = {StreamFlow},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/Taz8qtzmkmn9ffT},

year  = {2022},

date = {2022-07-01},

address = {Virtual event},

howpublished = {2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses},

note = {Invited talk},

keywords = {across, deephealth, eupex, streamflow, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:cip:romania,

title = {EuroHPC and the Italian HPC ecosystem},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/5dFFoNsZzwTzQkn},

year  = {2022},

date = {2022-06-01},

address = {Bucharest, 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.},

howpublished = {Critical Infrastructure Protection Forum - EuroCC Romania},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:eocoe:summit,

title = {The Italian HPC ecosystem and the next generation of EuroHPC CoE},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/AH5Ms3NekeoEooB},

year  = {2022},

date = {2022-06-01},

address = {Napoli, Italy},

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.},

howpublished = {EuroHPC EoCoE final summit},

note = {Invited talk},

keywords = {across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:textarossa-ga-meeting,

title = {T4.1: Streaming models},

author = {Iacopo Colonnelli and Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/cNBnwSnTc8GiCkN},

year  = {2022},

date = {2022-06-01},

address = {Roma, Italy},

howpublished = {TEXTAROSSA General Meeting},

keywords = {textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:elixir-streamflow,

title = {StreamFlow: a topology-aware WMS},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/Z9GsKnRCxmBdMd3},

year  = {2022},

date = {2022-06-01},

address = {Virtual event},

howpublished = {ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls},

note = {Invited talk},

keywords = {across, deephealth, eupex, streamflow, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:p3s:capio,

title = {From small files to no files},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/KLDi87xQmX86iXg},

year  = {2022},

date = {2022-06-01},

address = {Paris, France},

abstract = {Modern distributed high-performance storage systems saturate the network bandwidth, and the margins for improvement at the software level are tiny. Due to metadata access, they might be troubled with massive access to small files. An example is the Software Heritage (SH) dataset, half petabytes of files with an average size of 3kBytes (Terabytes of metadata). While working with SH, we developed the idea of substituting files with in-memory streams. We did it living in dread with the fear of asking application programmers to rewrite their lovely antique legacy code exploiting the POSIX interface, and up to now, we did not. In the talk, we will introduce CAPIO (Cross-Application Programmable I/O) design principles and the current state of development of the prototype.},

howpublished = {6th Workshop on Performance and Scalability of Storage Systems},

note = {Invited talk},

keywords = {admire, eupex},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:ml4astro,

title = {HPC-cloud convergence is the missing link between scientific computing and applied-AI},

author = {Marco Aldinucci},

editor = {Filomena Bufano and Simone Riggi and Eva Sciacca and Francesco Schilliro},

url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},

doi = {10.1007/978-3-031-34167-0_39},

year  = {2022},

date = {2022-06-01},

booktitle = {Astrophysics and Space Science Proceedings},

volume = {60},

pages = {193–199},

publisher = {Springer},

address = {Catania, Italy},

abstract = {First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.},

howpublished = {Machine Learning for Astrophysics (ML4ASTRO)},

note = {Keynote talk},

keywords = {across, deephealth, eupex, eupilot},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:PyCon,

title = {Python for the human heart: a case study},

author = {Gianluca Mittone},

url = {https://datacloud.di.unito.it/index.php/s/fERJB5xeE6cXm4N},

year  = {2022},

date = {2022-05-01},

address = {Florence, Italy},

abstract = {Presentation of the "Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets" paper at the PyCon22 DataBeer event.},

howpublished = {2022 Python Conference},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:hipeacvision:fl,

title = {Cognitive continuum: a game theoretical approach},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/453HWfmrQyo7j9E},

year  = {2022},

date = {2022-05-01},

address = {Brussels, Belgium},

abstract = {Cognitive continuum: a game theoretical approach, (maybe) data operations are too basic: read, write, copy, remove … The talk is aimed to contribute to the forthcoming HiPEAC Vision document},

howpublished = {HiPEAC Vision meeting, Brussels, 16 May 2022},

keywords = {across, admire, brainteaser, eumaster4hpc, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:garr,

title = {Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/P3KSroSSmrRxZMc},

year  = {2022},

date = {2022-05-01},

address = {Palermo, Italy},

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.},

howpublished = {Condivisioni, Conferenza GARR 2022},

note = {Keynote talk},

keywords = {across, admire, eumaster4hpc, eupex, hpc4ai, icsc, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:eupex-streamflow,

title = {StreamFlow: A framework for hybrid workflows},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/NjKEySP7HfrCQHZ},

year  = {2022},

date = {2022-04-01},

address = {Virtual event},

howpublished = {EUPEX WP5 bi-weekly meeting},

keywords = {eupex, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:jotb22,

title = {OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes},

author = {Iacopo Colonnelli and Dario Tranchitella},

url = {https://datacloud.di.unito.it/index.php/s/n6J7STNnwdyqtET},

year  = {2022},

date = {2022-04-01},

address = {Malaga, Spain},

howpublished = {J on The Beach 2022},

keywords = {across, deephealth, hpc4ai, jupyter-workflow, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:jotb22-workshop,

title = {Distributed workflows with Jupyter},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/om89q55S6ePf2Ji},

year  = {2022},

date = {2022-04-01},

address = {Malaga, Spain},

howpublished = {J on The Beach 2022},

note = {Workshop},

keywords = {across, deephealth, jupyter-workflow, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:lincei,

title = {Parallel Computing: a simple introduction, indeed an abstraction},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/nmaESJrapo2NqHq},

year  = {2022},

date = {2022-03-01},

address = {Virtual event},

howpublished = {I Lincei per la Scuola: scienze per l'innovazione digitale},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:visapp,

title = {Transfer Learning via Test-Time Neural Networks Aggregation},

author = {Bruno Casella},

url = {https://datacloud.di.unito.it/index.php/s/AKMT87eZDFX46JJ},

year  = {2022},

date = {2022-02-01},

address = {Virtual event},

abstract = {Presentation of the paper "Transfer Learning via Test-Time Neural Networks Aggregation" to VISAPP, the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},

howpublished = {VISAPP},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:across-streamflow,

title = {StreamFlow: A framework for hybrid workflows},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/FXFTKtQSRf6anMX},

year  = {2022},

date = {2022-02-01},

address = {Virtual event},

howpublished = {ACROSS WP4 meeting},

keywords = {across, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:VISIGRAPP,

title = {Poster Session VISIGRAPP 2022},

author = {Bruno Casella},

year  = {2022},

date = {2022-02-01},

address = {Torino, Italy},

howpublished = {VISIGRAPP 2022},

note = {Session chair},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{22:DHWinterSchool,

title = {The OpenDeepHealth toolkit},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/cJ8pRNsWRrfwPqr},

year  = {2022},

date = {2022-01-01},

address = {Torino, Italy},

howpublished = {DeepHealth Winter School},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:vertiv,

title = {[No title]},

author = {Marco Aldinucci and Sergio Rabellino},

url = {https://datacloud.di.unito.it/index.php/s/y6afrJr9w2DTmRN},

year  = {2021},

date = {2021-11-01},

address = {Milano, Italy},

howpublished = {Vertiv keep it running tour},

note = {Invited talk},

keywords = {hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:across-streamflow,

title = {StreamFlow: A framework for hybrid workflows},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/yrGYJL6CyNywF8a},

year  = {2021},

date = {2021-10-01},

address = {Virtual event},

howpublished = {ACROSS WP4 meeting},

keywords = {across, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:eni:streamflow,

title = {The modernization of HPC applications for the cloud era},

author = {Marco Aldinucci},

year  = {2021},

date = {2021-09-01},

address = {Virtual event},

abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g., clouds, supercomputers, and both of them. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments (such as Kubernetes and SLURM), making it possible to execute onto multiple sites not sharing a common data space. Streamflow clearly distinguishes it from many other workflow management systems because it decouples the data dependencies from the deployment of (containerized) workflow steps. Streamflow also leverages CAPIO (Cross-Application Programmable I/O) to move data from one step to another efficiently. CAPIO captures the POSIX file system and streams it in parallel and in-memory to the workflow's next step, possibly enabling in-transit data filtering.},

howpublished = {Fifth EAGE Workshop on High Performance Computing for Upstream},

note = {Keynote talk},

keywords = {across, admire, deephealth, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:across-containers,

title = {HPC Containers},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/ddf3YBjpm8KBGAF},

year  = {2021},

date = {2021-07-01},

address = {Virtual event},

howpublished = {ACROSS WP4 meeting},

keywords = {across},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:hlpp:streamflow,

title = {From skeletons to workflows in the cloud-edge era},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/RyRPjNBse5PKnab},

year  = {2021},

date = {2021-07-01},

address = {Virtual event},

abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments and that makes it possible to execute multiple sites not sharing a common data space. StreamFlow supports both task and data parallelism and enables the reproducible and scalable execution of workflows, such as AI pipelines, in hybrid cloud-HPC environments. As a running example, we use the novel ``universal COVID-19 pipeline'' that explore the whole optimisation space of the training of different DNNs to classify COVID-19 lung lesions.},

howpublished = {14th Intl. Symposium on High-Level Programming and Applications (HLPP)},

note = {Keynote talk},

keywords = {across, admire, deephealth, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:penta:covid,

title = {Reproducibility in the AI era},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/GLpf7kKSJRH733A},

year  = {2021},

date = {2021-07-01},

address = {Virtual event},

abstract = {TBD},

howpublished = {Penta Scientific Meeting},

keywords = {across, admire, deephealth},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:dataweek:deephealth,

title = {DeepHealth perspective},

author = {Marco Aldinucci},

year  = {2021},

date = {2021-05-01},

address = {Virtual event},

abstract = {TBD},

howpublished = {Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data – BDVA Data Week},

keywords = {deephealth},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:CINI_acm_CF_talk,

title = {The Italian research on HPC key technologies across EuroHPC},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/3ZYmDbEm84rbB9k},

year  = {2021},

date = {2021-05-01},

booktitle = {ACM Computing Frontiers},

publisher = {ACM},

address = {Virtual Conference, Italy},

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.},

keywords = {across, admire, eupex, eupilot, textarossa},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:poloict:deephealth,

title = {DeepHealth: Deep Learning ad alte prestazioni per applicazioni in ambito medico},

author = {Marco Aldinucci and Marco Beccuti},

url = {https://datacloud.di.unito.it/index.php/s/2F5Net5HdfJTysa},

year  = {2021},

date = {2021-04-01},

address = {Torino, Italy},

howpublished = {Reserach meeting of the PoloICT},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:poloict_hpc4ai,

title = {HPC4AI: Un sistema per la ricerca e l'innovazione dei servizi cloud per l'Intelligenza Artificiale},

author = {Marco Aldinucci and Marco Beccuti},

url = {https://datacloud.di.unito.it/index.php/s/BXdXLzsisQwDLrK},

year  = {2021},

date = {2021-04-01},

address = {Torino, Italy},

howpublished = {Reserach meeting of the PoloICT},

keywords = {hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:gtc:clairecovid,

title = {The Universal Cloud-HPC Pipeline for the AI-Assisted Explainable Diagnosis of COVID-19 Pneumonia},

author = {Marco Aldinucci and Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/AkQLbPpEEtDzbbm},

year  = {2021},

date = {2021-04-01},

address = {Virtual event},

abstract = {We'll present a methodology to run DNN pipelines on hybrid cloud+HPC infrastructure. We'll also define a "universal pipeline" for medical images. The pipeline can reproduce all state-of-the-art DNNs to diagnose COVID-19 pneumonia, which appeared in the literature during the first Italian lockdown and following months. We can run all of them (across cloud+HPC platforms) and compare their performance in terms of sensitivity and specificity to set a baseline to evaluate future progress in the automated diagnosis of COVID-19. Also, the pipeline makes existing DNNs explainable by way of adversarial training. The pipeline is easily portable and can run across different infrastructures, adapting the performance-urgency trade-off. The methodology builds onto two novel software programs: the streamflow workflow system and the AI-sandbox concept (parallel container with user-space encrypted file system). We reach over 92% accuracy in diagnosing COVID pneumonia.},

howpublished = {NVidia GTC'21},

note = {Invited talk},

keywords = {deephealth, hpc4ai, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:CWLMiniConference,

title = {StreamFlow: cross breeding cloud with HPC},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/Le9gg4PfjRxBwXD},

year  = {2021},

date = {2021-02-01},

address = {Virtual event},

abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space.},

howpublished = {2021 CWL Mini Conference},

note = {Invited talk},

keywords = {deephealth, streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:CNR:hpcai,

title = {On HPC, AI and their Fatal Attraction},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/pSDxNPncic8gEy8},

year  = {2021},

date = {2021-02-01},

address = {Virtual event},

howpublished = {CNR IEIIT, Thursday seminars (11 Feb 2021)},

note = {Invited talk},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:dell:hpc4ai,

title = {HPC4AI: A cloud-HPC ecosystem designed for research and innovation},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/NmM9kB42pJZGMx6},

year  = {2021},

date = {2021-01-01},

abstract = {The University of Turin and Polytechnic University of Turin have joined forces to create a federated competence centre on High-Performance Computing (HPC), Artificial Intelligence (AI) and Big Data Analytics (BDA). HPC4AI is designed as a centre capable of collaborating with entrepreneurs to boost their ability to innovate on data-driven technologies and applications. HPC4AI started in 2017 with the construction of four new federated computing laboratories completed at the end of 2020. HPC4AI is organized in two poles: one at the University of Turin (UNITO), which coordinated the design and implementation phase of HPC4AI, and another at the Polytechnic of Turin (POLITO).},

howpublished = {Advanced Computing Workshop 2021: HPC and Beyond},

note = {Invited talk},

keywords = {hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:icpr:demodeephealth,

title = {Lung Nodules Segmentation in CT scans by DeepHealth toolkit},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/KYJMcT3pfpat2Hx},

year  = {2021},

date = {2021-01-01},

publisher = {BDVA},

address = {Milano. Italy},

howpublished = {25th Intl. Conference on Pattern Recognition},

note = {Demo},

keywords = {deephealth},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:parmaditam:hpc4ai,

title = {HPC application cloudification: the streamflow toolkit},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/HWZijXPqmwfoYCp},

year  = {2021},

date = {2021-01-01},

address = {Virtual event},

howpublished = {PARMA-DITAM (co-localed with HiPEAC)},

note = {Keynote talk},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{21:covid:aihub,

title = {High-performance computing and AI team up for COVID-19 diagnostic imaging},

author = {Marco Aldinucci},

url = {https://aihub.org/2021/01/12/high-performance-computing-and-ai-team-up-for-covid-19-diagnostic-imaging/},

year  = {2021},

date = {2021-01-01},

abstract = {The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) taskforce on AI & COVID-19 supported the creation of a research group focused on AI-assisted diagnosis of COVID-19 pneumonia. The first results demonstrate the great potential of AI-assisted diagnostic imaging. Furthermore, the impact of the taskforce work is much larger, and it embraces the cross-fertilisation of artificial intelligence (AI) and high-performance computing (HPC): a partnership with rocketing potential for many scientific domains.},

howpublished = {AIhub},

note = {(magazine)},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:GarrWorkshop,

title = {JupyterFlow: Jupyter Notebooks su larga scala},

author = {Iacopo Colonnelli and Sergio Rabellino},

url = {https://datacloud.di.unito.it/index.php/s/ASPEmyXAj5QscgC},

year  = {2020},

date = {2020-11-01},

address = {Virtual event},

abstract = {I Jupyter Notebook sono largamente utilizzati sia in ambito industriale che accademico come strumento di didattica, prototipazione e analisi esplorative. Purtroppo il sistema runtime standard di Jupyter non è abbastanza potente per sostenere un carichi di lavoro reali e spesso l'unica soluzione è quella di riscrivere il codice da zero in una tecnologia con supporto HPC. Intrgrando lo stack Jupyter con StreamFlow (https://streamflow.di.unito.it/) è possibile creare i Notebook tramite un'interfaccia web su cloud ed eseguirli in maniera trasparente in remoto su una VM con GPU o su nodi HPC.},

howpublished = {Workshop GARR 2020},

keywords = {deephealth, hpc4ai, jupyter-workflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:covid:ag,

title = {Polmonite da COVID-19, diagnosi con l'intelligenza artificiale: Italia in prima fila},

author = {Marco Aldinucci},

url = {https://www.agendadigitale.eu/sanita/polmonite-da-covid-19-allo-studio-la-diagnosi-tramite-intelligenza-artificiale-italia-in-prima-fila/},

year  = {2020},

date = {2020-11-01},

abstract = {La Task Force su AI&COVID-19 della confederazione europea dei laboratori di ricerca sull'intelligenza artificiale (CLAIRE) ha sostenuto la creazione di un gruppo di ricerca focalizzato sulla diagnosi della polmonite da COVID assistita dall'Intelligenza Artificiale. I primi risultati sono incoraggianti},

howpublished = {Agenda Digitale},

note = {(magazine)},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:HPCEuropa3TAM,

title = {StreamFlow: cross breeding cloud with HPC},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/qPHHrSNxk8QXJDw},

year  = {2020},

date = {2020-10-01},

address = {Virtual event},

abstract = {Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.},

howpublished = {HPC-Europa3 2nd Transnational Access Meeting (TAM)},

note = {Invited talk},

keywords = {streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:bdva:deephealth,

title = {The DeepHealth project},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ortwLJHS2q96irb},

year  = {2020},

date = {2020-07-01},

publisher = {BDVA},

howpublished = {HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics virtual workshop — HPC / HPDA spectrum},

note = {Invited talk},

keywords = {deephealth},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:claire:taskforce,

title = {Building avenues for AI-assisted diagnosis over the bridge from HPC to AI},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/RqpNCHyyL6wc5ds},

year  = {2020},

date = {2020-07-01},

address = {Torino, Italy},

howpublished = {CLAIRE COVID webinar},

note = {Invited talk},

keywords = {deephealth},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:CIE:streamflow,

title = {StreamFlow: cross-breeding cloud with HPC},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/Ltqo4SmJj42wjyo},

year  = {2020},

date = {2020-06-01},

howpublished = {Computability in Europe 2020 (CIE)},

note = {Invited talk},

keywords = {deephealth},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:chem:HPCAI,

title = {Machine Learning: the treacherous journey from data to knowledge (with examples from HPC4AI@UNITO platform)},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ffyZYYqNQpkza4F},

year  = {2020},

date = {2020-02-01},

address = {Torino, Italy},

howpublished = {Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino},

note = {Invited talk},

keywords = {deephealth, hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:elixir:hpc4ai,

title = {HPC4AI: From Enabling Platforms to Technology Sovereignty to Innovation},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/g6LZiErXH4PPRCj},

year  = {2020},

date = {2020-02-01},

address = {Torino, Italy},

howpublished = {Elixir-Italia meeting},

note = {Invited talk},

keywords = {hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:hlpgpu:mnemocomputing,

title = {Mnemocomputing},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/8pAGGsw4nEtrtNj},

year  = {2020},

date = {2020-01-01},

address = {Valencia, Spain},

howpublished = {HLPGPU 2019 (Satellite workshop of HiPEAC 2019)},

note = {Keynote talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:ICAIH:AIhealth,

title = {How Artificial Intelligence is shaping the Future of Health},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/FPDCjcWwprAa7D7},

year  = {2019},

date = {2019-11-01},

address = {Milano, Italy},

howpublished = {1st Industrial Conference on Artificial Intelligence and health},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:GarrWorkshop,

title = {StreamFlow: un approccio dichiarativo a workflow e pipeline di micro-servizi},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/kZqyiQnBEQNdXJe},

year  = {2019},

date = {2019-10-01},

address = {Roma, Italy},

abstract = {Negli ultimi anni, gli approcci orientati ai container si sono dimostrati particolarmente efficaci nel garantire portabilità e riproducibilità dei workflow scientifici. Tuttavia, con il continuo aumento del volume di dati a disposizione e la crescente complessità delle procedure di analisi in ogni campo della ricerca, anche i requisiti di performance e riusabilità si fanno via via sempre più essenziali. L'obiettivo principale di StreamFlow è quello di fornire un nuovo paradigma, totalmente dichiarativo, per la descrizione e l'accelerazione di workflow scientifici in ambienti distribuiti. La peculiarità di StreamFlow risiede nel fatto che l'ambiente di esecuzione è interamente descritto in termini di servizi (container), connessioni tra essi e fattori di replica. Inoltre, ogni task del workflow è esplicitamente mappato sulla tipologia di servizio richiesta. Questo permette un maggior controllo sull'utilizzo delle risorse e politiche di scheduling più precise, a vantaggio delle performance. I principali vantaggi di un approccio dichiarativo sono invece la più facile comprensione ed estensione dei modelli esistenti, a vantaggio della riusabilitià.},

howpublished = {Workshop GARR 2019},

keywords = {streamflow},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:SPPEXA:DLscale,

title = {Towards DeepLearning at Scale},

author = {Marco Aldinucci},

year  = {2019},

date = {2019-10-01},

address = {Dresden, Germany},

howpublished = {SPPEXA Final Symposium},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:APRE:EuroHPC,

title = {The Joint Undertaking EuroHPC},

author = {Marco Aldinucci},

year  = {2019},

date = {2019-10-01},

address = {Roma, Italy},

howpublished = {Giornata Nazionale di Lancio dei bandi Information and Communication Technologies in Horizon 2020},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:SR:hpc4ai,

title = {HPC4AI, an on-demand federated platform endeavour},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/P7fxgExkJDAFQbm},

year  = {2019},

date = {2019-05-01},

address = {Milano, Italy},

howpublished = {Ospedale San Raffaele},

note = {Invited talk},

keywords = {hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:conference:PublicBD,

title = {L'infrastruttura necessaria per creare interoperabilità tra pubbliche amministrazioni},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/KPebr76m88jQATi},

year  = {2019},

date = {2019-05-01},

address = {Turin, Italy},

howpublished = {Convegno ``L'amministrazione pubblica con i Big data''},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:iCom:HPC,

title = {High-Performance Computing},

author = {Marco Aldinucci},

url = {https://www.i-com.it/2019/02/02/supercomputer-hpe-computing/},

year  = {2019},

date = {2019-04-01},

address = {Roma, Italy},

howpublished = {Istituto per la Competitività},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:italia,

title = {Deep Learning e calcolo ad alte prestazioni per l'elaborazione di immagini biomediche},

author = {Marco Aldinucci and Claudio Berzovini and Costantino Grana and Marco Grangetto and Luca Pireddu and Gianluigi Zanetti},

year  = {2019},

date = {2019-03-01},

abstract = {Il progetto DeepHealth, recentemente finanziato dalla Commissione Europea, ha come obiettivo la realizzazione di un ecosistema europeo costituito da piattaforme di calcolo ad alte prestazioni, librerie software e competenze multi-disciplinari di intelligenza artificiale, calcolo parallelo e scienze mediche per l'elaborazione e la diagnosi basata su immagini. Il contributo presenta sinteticamente le competenze e le infrastrutture nazionali coinvolte nel progetto.},

howpublished = {Ital-IA: Convegno Nazionale CINI sull'Intelligenza Artificiale},

keywords = {hpc4ai},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:PDPNNT,

title = {Deep Learning at Scale},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/nRW9M69C3AtpDoM},

year  = {2019},

date = {2019-02-01},

publisher = {IEEE},

address = {Pavia, Italy},

abstract = {This work presents a novel approach to distributed training of deep neural networks (DNNs) that aims to overcome the issues related to mainstream approaches to data parallel training. Established techniques for data parallel training are discussed from both a parallel computing and deep learning perspective, then a different approach is presented that is meant to allow DNN training to scale while retaining good convergence properties. Moreover, an experimental implementation is presented as well as some preliminary results.},

howpublished = {27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:PDPArmadillo,

title = {Accelerating spectral graph analysis through wavefronts of linear algebra operations},

author = {Iacopo Colonnelli},

url = {https://datacloud.di.unito.it/index.php/s/zK4eSzdsdB8CfQX},

year  = {2019},

date = {2019-02-01},

publisher = {IEEE},

address = {Pavia, Italy},

abstract = {The wavefront pattern captures the unfolding of a parallel computation in which data elements are laid out as a logical multidimensional grid and the dependency graph favours a diagonal sweep across the grid. In the emerging area of spectral graph analysis, the computing often consists in a wavefront running over a tiled matrix, involving expensive linear algebra kernels. While these applications might benefit from parallel heterogeneous platforms (multi-core with GPUs),programming wavefront applications directly with high-performance linear algebra libraries yields code that is complex to write and optimize for the specific application. We advocate a methodology based on two abstractions (linear algebra and parallel pattern-based run-time), that allows to develop portable, self-configuring, and easy-to-profile code on hybrid platforms.},

howpublished = {27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{19:SmartData:fromHPCtoDL,

title = {The evolution of high-performance systems: from HPC to Big Data to Deep Learning},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/cZitMsp5GPJ2Qzf},

year  = {2019},

date = {2019-02-01},

address = {Torino, Italy},

howpublished = {4th Open SmartData@PoliTO Workshop},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{18:ChimicaPP:fromHPCtoDL,

title = {L'evoluzione delle piattaforme e dei sistemi ad alte prestazioni: da HPC ai Big Data al Deep Learning},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/sLxm6G2Ma5jj9BP},

year  = {2018},

date = {2018-11-01},

address = {Torino, Italy},

howpublished = {Chimica passione periodica: Big Data: Modelli predittivi, simulazione, analisi, Dipartimento di Chimica, Università di Torino},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{18:CITYLAB:AIonDemand,

title = {Le piattaforme AI-on-demand come fattore di l'innovazione nelle PMI},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/Twy9zwbJB9KnBE2},

year  = {2018},

date = {2018-06-01},

address = {Torino, Italy},

howpublished = {CITYLAB Ecosystem: i dati per le aziende, le città e le università, Nuvola Lavazza Torino},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{18:ACM:HPC4AI,

title = {HPC4AI, an AI-on-demand federated platform endeavour},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/NWbZ2FYpeYR47T6},

year  = {2018},

date = {2018-05-01},

address = {Ischia, Italy},

howpublished = {ACM Computing Frontiers},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{18:PDP:NDP,

title = {Toward Near-Data Processing service computing},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ePDZ44KSsgXSGHw},

year  = {2018},

date = {2018-03-01},

publisher = {IEEE},

address = {Cambridge, UK},

howpublished = {Parallel, Distributed, and Network-Based Processing (PDP)},

note = {Keynote talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{18:ARM:energyEfficiency,

title = {From mutexes to lock-free to atomic-less and back to (un-contended) mutex: A story in which energy efficiency is unexpectedly constantly increasing},

author = {Marco Aldinucci},

year  = {2018},

date = {2018-03-01},

address = {Cambridge, UK},

howpublished = {Arm Research ltd},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{18:DISIA:federated,

title = {Designing a heterogeneous federated data center for research},

author = {Marco Aldinucci},

year  = {2018},

date = {2018-03-01},

address = {Firenze, Italy},

howpublished = {DiSIA, University of Florence},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{20:parco:PGAS,

title = {Partitioned Global Address Space in the mainstream of C++ programming},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/dTAm7oAfHRTY7BC},

year  = {2017},

date = {2017-09-01},

address = {Bologna, Italy},

howpublished = {International Parallel Computing (ParCo)},

note = {Keynote talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{17:INFN:occam,

title = {OCCAM: Heterogeneous platforms mixed blessing of code optimisation},

author = {Marco Aldinucci},

url = {https://agenda.infn.it/login/?next=%2Fevent%2F12983%2Ftimetable%2F%3Fview%3Dstandard},

year  = {2017},

date = {2017-09-01},

address = {Torino, Italy},

howpublished = {Riunione della Commissione Calcolo e Reti dell'INFN},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{17:autodasp:17,

title = {Streaming in the PGAS Era},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/H6ZRoK6gLZDQFta},

year  = {2017},

date = {2017-08-01},

address = {Santiago de Compostela, Spain},

howpublished = {International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017), Workshop of EuroPar 2017},

note = {Keynote talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{17:EUROPAR:presentation,

title = {Designing a heterogeneous federated data center for research},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/DyJ3A6Hgj52e33b},

year  = {2017},

date = {2017-08-01},

address = {Santiago di Compostela, Spain},

howpublished = {Euro-Par 2017},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{16:CARLOALBERTO:dataScience,

title = {HPC come piattaforma abilitante: rischi e opportunità},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/6qgoQoSXaZpMQq4},

year  = {2016},

date = {2016-10-01},

address = {Torino, Italy},

howpublished = {Workshop of the Competence Centre on Scientific Computing of University of Torino},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{16:ARM:atomic,

title = {Atomic operations considered hARMful},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/mBoWAmMzj8nYrbe},

year  = {2016},

date = {2016-09-01},

address = {Cambridge, UK},

howpublished = {ARM Research Summary},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{16:PintOfScience:exascale,

title = {I supercomputer venuti dal futuro: Exascale Computing},

author = {Marco Aldinucci},

year  = {2016},

date = {2016-05-01},

address = {Torino, Italy},

howpublished = {International Pint of Science, Officine ferroviarie},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{16:SIAM:composition,

title = {Composition and Compartmentalisation As Enabling Features for Data-Centric, Extreme Scale Applications: An MPI-X Approach},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/cWne6zEWypepo7H},

year  = {2016},

date = {2016-04-01},

address = {Paris, France},

howpublished = {SIAM Conference on Parallel Processing for Scientific Computing},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{16:UNITO:fastdata,

title = {[No title]},

author = {Marco Aldinucci},

year  = {2016},

date = {2016-03-01},

address = {Torino, Italy},

howpublished = {FastData@UNITO opening},

note = {Invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{14:hpcc:datacentric,

title = {Parallel patterns, data-centric concurrency, and heterogeneous computing},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/mRSTsB6qjKMYiEN},

year  = {2014},

date = {2014-08-01},

address = {Paris, France},

howpublished = {6th IEEE Intl. Conference on High-Performance Computing and Communications (HPCC)},

note = {Keynote talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{14:dublin:ff,

title = {FastFlow: high-level programming patterns with non-blocking lock-free run-time support},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/TJP46RfwX2NGPTS},

year  = {2014},

date = {2014-06-01},

address = {Dublin, Ireland},

howpublished = {Intl. Summer School in Parallel Patterns},

note = {Keynote talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{14:GTC:overview,

title = {An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/sRYnknEHn3Qkj5b},

year  = {2014},

date = {2014-03-01},

address = {San Jose, CA, USA},

howpublished = {GPU Technology Conference (GTC 2014)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{14:GTC:fastflow,

title = {FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ibCGJc7tJyASMbN},

year  = {2014},

date = {2014-03-01},

address = {San Jose, CA, USA},

howpublished = {GPU Technology Conference (GTC 2014)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{13:RAF:cloud,

title = {Turning Big data into knowledge: systems biology droplets in the cloud},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/oBPqA5DeyCbj5do},

year  = {2013},

date = {2013-03-01},

address = {Madrid, Spain},

howpublished = {Cloud and Science seminar at ``the Ramon Areces Foundation''},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{12:IEEE:denoising,

title = {A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/JQe8RDp9jp4iymF},

year  = {2012},

date = {2012-10-01},

address = {Istanbul, Turkey},

howpublished = {IEEE Intl. Conference on Image Processing Theory, Tools and Applications (IPTA)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{12:BIOIT:bigdata,

title = {Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/BjLQyiDofH6gB3d},

year  = {2012},

date = {2012-10-01},

address = {Vienna, Austria},

howpublished = {Bio-IT World Europe},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{12:UPMARC:fastflow,

title = {FastFlow: high-level programming patterns with non-blocking lock-free run-time support},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/BWGrrsTWidMtcCn},

year  = {2012},

date = {2012-09-01},

address = {Uppsala, Sweden},

howpublished = {UPMARC Workshop on Task-Based Parallel Programming},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{12:bigdata:ff,

title = {Turning Big data into knowledge: Techniques and Tools for Parallel Computing on Online Data Streams in Systems Biology and Epidemiology},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/yg7i63MQdr7gZt7},

year  = {2012},

date = {2012-08-01},

address = {Rhodes, Greece},

howpublished = {Workshop in big data management},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{12:HPCAC:denoising,

title = {Pattern-based Parallel Edge Preserving Algorithm for Salt-and-Pepper Image Denoising},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/BCm7w9S86yDNJb2},

year  = {2012},

date = {2012-03-01},

address = {Lugano, Switzerland},

howpublished = {HPC Advisory Council Switzerland Conference},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:TOSM:denoiser,

title = {The camera that makes you beauty (with a novel real-time video-denoiser algorithm)},

author = {Marco Aldinucci},

year  = {2011},

date = {2011-11-01},

address = {Torino, Italy},

howpublished = {TOSM expo},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:IMT:muticore,

title = {Performance and productivity in the multi-core era: challenges in software engineering and formal methods},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/Ppok8Cno7enc6QT},

year  = {2011},

date = {2011-11-01},

address = {Lucca, Italy},

howpublished = {IMT},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:IBM:fastflow,

title = {FastFlow: Performance and Productivity in the Exascale Era},

author = {Marco Aldinucci},

year  = {2011},

date = {2011-10-01},

address = {Dublin, Ireland},

howpublished = {IBM Research, Exascale laboratory},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:FMCO:fastflow,

title = {FastFlow: Performance and Productivity in the Multicore Era},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/3opjTzm6XRcjYEs},

year  = {2011},

date = {2011-10-01},

address = {Torino, Italy},

howpublished = {Formal Methods for Components and Objects (FMCO)},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:notteRicercatori:myMed,

title = {MyMed: Un social network geosensibile per reti fisse e mobili basato sul paradigma Peer-to-Peer},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/QjcSysM93X9DCDn},

year  = {2011},

date = {2011-09-01},

address = {Torino, Italy},

howpublished = {La notte dei ricercatori},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:HiBB:simulation,

title = {On Parallelizing On-Line Statistics for Stochastic Biological Simulations},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/3ijP5zx2ktT6Xab},

year  = {2011},

date = {2011-08-01},

address = {Bordeaux, France},

howpublished = {Workshop on High Performance Bioinformatics and Biomedicine (HiBB)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:EUROPAR:fastflow,

title = {Accelerating code on multi-cores with FastFlow},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/Z4RtA9QoxCm829j},

year  = {2011},

date = {2011-08-01},

address = {Bordeaux, France},

howpublished = {Euro-Par 2011},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:COST:parallel,

title = {High-level parallel programming: (few) ideas for challenges in formal methods},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/XfikGmd2e6pwfaM},

year  = {2011},

date = {2011-06-01},

address = {Limerick, Republic of Ireland},

howpublished = {COST Action IC701 workshop},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{11:IEEE:simulator,

title = {On Designing Multicore-Aware Simulators for Biological Systems},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/Anb4Gdj8p9yq4C2},

year  = {2011},

date = {2011-02-01},

address = {Ayia Napa, Ciprus},

howpublished = {IEEE Euromicro PDP 2011: Parallel Distributed and network-based Processing},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{10:SCHOSS:fastflow,

title = {FastFlow: a pattern-based programming framework for multicores},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/fMpTjXat97kp2Hp},

year  = {2010},

date = {2010-05-01},

address = {Wadern, Germania},

howpublished = {seminar 10191},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{10:PDP:fastflow,

title = {Efficient Smith-Waterman on multi-core with FastFlow},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/2RM8k6j3QGo5R7C},

year  = {2010},

date = {2010-02-01},

address = {Pisa, Italy},

howpublished = {PDP 2010: Parallel Distributed and network-based Processing},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{09:PARCO:fastflow,

title = {Efficient streaming applications on multi-core with FastFlow: the biosequence alignment test-bed},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/Aa26ZyrfSB7ZWzj},

year  = {2009},

date = {2009-09-01},

address = {Lyon, France},

howpublished = {ParCo 2009},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{09:EUROPAR:stkm,

title = {STKM on SCA: A unified framework with components, workflows and algorithmic skeletons},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/KDyBfqmt9HXXwKT},

year  = {2009},

date = {2009-09-01},

address = {Delft, The Netherlands},

howpublished = {EuroPar 2009},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{09:PDP:hierarchical,

title = {Towards Hierarchical Management of Autonomic Components: a Case Study},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/KTn3dGwQwbe3bMc},

year  = {2009},

date = {2009-02-01},

address = {Weimar, Germany},

howpublished = {Euromicro PDP 2009: Parallel Distributed and network-based Processing},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{gridcomp:D.NFCF.05,

title = {D.NFCF.05 – NFCF tuned prototype and final documentation},

author = {Marco Aldinucci and Sonia Campa and Patrizio Dazzi and Nicola Tonellotto and Giorgio Zoppi},

year  = {2009},

date = {2009-01-01},

howpublished = {url http://gridcomp.ercim.org/},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{08:COREGRID:semantics,

title = {Towards a Formal Semantics for Autonomic Components},

author = {Marco Aldinucci},

year  = {2008},

date = {2008-08-01},

address = {Las Palmas de Gran Canaria, Canary Island, Spain},

howpublished = {CoreGRID Symposium},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{gridcomp:D.NFCF.03,

title = {D.NFCF.03 – Methodology to derive performance models for component and composite components},

author = {Marco Aldinucci and Sonia Campa and Patrizio Dazzi and Nicola Tonellotto and Giorgio Zoppi},

year  = {2008},

date = {2008-06-01},

howpublished = {url http://gridcomp.ercim.org/},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{gridcomp:D.NFCF.04,

title = {D.NFCF.04 – NFCF prototype and early documentation},

author = {Marco Aldinucci and Sonia Campa and Patrizio Dazzi and Nicola Tonellotto and Giorgio Zoppi},

year  = {2008},

date = {2008-06-01},

howpublished = {url http://gridcomp.ercim.org/},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{ogf:cg:poster:08,

title = {CoreGRID Institute on Programming Model},

author = {Marco Aldinucci},

url = {http://calvados.di.unipi.it/storage/paper_files/2008_CG_PM_OGF.pdf},

year  = {2008},

date = {2008-06-01},

address = {Barcelona, Spain},

howpublished = {Open Grid Forum (OGF), CoreGRID Industrial Showcase},

note = {Poster},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{08:COREGRID:autonomic,

title = {Autonomic components in GCM},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/22S3LwsTjbkG3w3},

year  = {2008},

date = {2008-05-01},

address = {Amsterdam, The Netherland},

howpublished = {CoreGRID Scientific Advisory Board},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{07:NSS:virtualLinux,

title = {VirtuaLinux: Una soluzione open source per il clustering HPC},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/4SWFntywGgDtoxx},

year  = {2007},

date = {2007-11-01},

address = {Pisa, Italia},

howpublished = {Net & System Security},

note = {invited talk in italian},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{gridcomp:D.NFCF.01,

title = {D.NFCF.01 – Non functional component subsystem architectural design},

author = {Marco Aldinucci and Sonia Campa and Patrizio Dazzi and Nicola Tonellotto},

year  = {2007},

date = {2007-06-01},

howpublished = {url http://gridcomp.ercim.org/},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{gridcomp:D.NFCF.02,

title = {D.NFCF.02 – Non functional component subsystem architectural design (code)},

author = {Marco Aldinucci and Sonia Campa and Patrizio Dazzi and Nicola Tonellotto},

year  = {2007},

date = {2007-06-01},

howpublished = {url http://gridcomp.ercim.org/},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{07:COREGRID:skeletons,

title = {Behavioural skeletons for component autonomic management on grids},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ZpidDEMJo8Pc4nB},

year  = {2007},

date = {2007-06-01},

address = {Heraklion, Crete, Greece},

howpublished = {CoreGRID Workshop on Grid Programming Model, Grid and P2P Systems Architecture, Grid Systems, Tools and Environments},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{06:LACS:adptation,

title = {Taming the grid through dynamic adaptation: results and open problems},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/2i8tDjMYEYnZeFo},

year  = {2006},

date = {2006-11-01},

address = {San Cristobal de la Laguna, Tenerife, Canarian Islands, Spain},

howpublished = {Last Advances in Computer Science},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{06:COREGRID:storage,

title = {Fault-tolerant data sharing for high-level grid programming: a hierarchical storage architecture},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/iibrkEtMREHiexA},

year  = {2006},

date = {2006-10-01},

address = {Krakow, Poland},

howpublished = {CoreGRID Integration Workshop},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{06:IEEE:qos,

title = {Autonomic QoS in ASSIST Grid-aware components},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/bcCddKkiF9KsDWM},

year  = {2006},

date = {2006-02-01},

address = {Montbéliard, France},

howpublished = {IEEE Euromicro PDP 2006: Parallel Distributed and network-based Processing},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{05:PARCO:assist,

title = {Building Interoperable Grid-aware ASSIST Applications via Web Services},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/6rbd7RWE6fbMbsi},

year  = {2005},

date = {2005-09-01},

address = {Malaga, Spain},

howpublished = {ParCo 2005},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{05:PARCO:dataService,

title = {Towards a distributed scalable data service for the Grid},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/zQMGHBAWRLdKsbH},

year  = {2005},

date = {2005-09-01},

address = {Malaga, Spain},

howpublished = {ParCo 2005},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{05:EUROPAR:assist,

title = {Dynamic reconfiguration of grid-aware applications in ASSIST},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/nPESNMGWirAYEFB},

year  = {2005},

date = {2005-09-01},

address = {Lisbon, Portugal},

howpublished = {Euro-Par 2005},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{04:FGG:heterogeneity,

title = {Rendering Grid Heterogeneity Harmless},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/ofZiZrd8YpWgqZD},

year  = {2004},

date = {2004-11-01},

address = {Dagstuhl, Germany},

howpublished = {Seminar 04451 – Future Generation Grids},

note = {invited talk},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{04:EUROPAR:apache,

title = {Accelerating Apache farms through ad-HOC distributed scalable object repository},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/zLXTRRQb2rJNyDy},

year  = {2004},

date = {2004-09-01},

address = {Pisa, Italy},

howpublished = {Euro-Par 2004},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{04:CMPP:skeletons,

title = {Optimization techniques for implementing parallel skeletons in Grid},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/MozKWbWPCxko2CX},

year  = {2004},

date = {2004-07-01},

address = {Stirling, Scotland},

howpublished = {CMPP 2004 (in conjunction with MPC 04)},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{03:PARCO:skeletons,

title = {An operational semantics for skeletons},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/xDnFpT6RM9LHAEd},

year  = {2003},

date = {2003-09-01},

address = {Dresden, Germany},

howpublished = {ParCo 2003},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{03:PARCO:framework,

title = {A framework for experimenting with structured parallel programming environment design},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/FkGk3giN8MiGMwo},

year  = {2003},

date = {2003-09-01},

address = {Dresden, Germany},

howpublished = {ParCo 2003},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{03:EUROPAR:assist,

title = {ASSIST demo: a high level, high performance, portable, structured parallel programming environment at work},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/fmEcpJDGD4iitia},

year  = {2003},

date = {2003-08-01},

address = {Austria},

howpublished = {9th Intl Euro-Par 2003: Parallel and Distributed Computing},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{03:HLPP:eskimo,

title = {eskimo: experimenting skeletons on the shared address model},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/noo2N7HBKWm2nMH},

year  = {2003},

date = {2003-06-01},

address = {Paris, France},

howpublished = {HLPP 2003},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{00:CMPP:META,

title = {The META transformation tool for skeleton-based language},

author = {Marco Aldinucci},

url = {https://datacloud.di.unito.it/index.php/s/e9L5fWDo9q5fmw4},

year  = {2000},

date = {2000-07-01},

address = {Ponte de Lima, Portugal},

howpublished = {CMPP 2000},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}