Talks | Parallel Computing
2024
Lorenzo Brescia
Secure workflow computation Miscellaneous
Presentation of previous works and future directions on securing workflows, 2024.
Links | BibTeX | Tags: confidential, icsc
@misc{24:brescia:swiss:talk,
title = {Secure workflow computation},
author = {Lorenzo Brescia},
url = {https://datacloud.di.unito.it/index.php/s/5nwZ2bi7by3twQB},
year = {2024},
date = {2024-11-01},
address = {Neuchatel, Switzerland},
howpublished = {Presentation of previous works and future directions on securing workflows},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {misc}
}
Roberto Esposito Mirko Polato Samuele Fonio
FedHP: Federated Learning with Hyperspherical Prototypical Regularization Miscellaneous
32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN), 2024.
Abstract | Links | BibTeX | Tags: ai, fl, icsc
@misc{24:esann:fedhp,
title = {FedHP: Federated Learning with Hyperspherical Prototypical Regularization},
author = {Roberto Esposito Mirko Polato Samuele Fonio},
url = {https://datacloud.di.unito.it/index.php/s/fKyKSSFQKT3LTxW},
year = {2024},
date = {2024-10-01},
address = {Bruges, Belgium},
abstract = {This paper introduces FedHP, an innovative algorithm that integrates federated learning, hyperspherical geometries, and prototype learning. Federated Learning (FL) has gained prominence as a privacy- preserving method for building robust models across distributed datasets. Traditionally, FL exchanges model parameters to maintain data privacy; however, in scenarios with expensive data communication, exchanging large neural network models becomes impractical. In such cases, prototype learning offers a viable solution by facilitating the exchange of only a few prototypes. Motivated by these considerations, our approach capitalizes on recent advancements in prototype learning, particularly the advantages offered by non-Euclidean geometries. In addition to presenting FedHP, we offer empirical evidence demonstrating its comparability to other state-of- the-art approaches while significantly reducing communication costs.},
howpublished = {32nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (ESANN)},
keywords = {ai, fl, icsc},
pubstate = {published},
tppubtype = {misc}
}
This paper introduces FedHP, an innovative algorithm that integrates federated learning, hyperspherical geometries, and prototype learning. Federated Learning (FL) has gained prominence as a privacy- preserving method for building robust models across distributed datasets. Traditionally, FL exchanges model parameters to maintain data privacy; however, in scenarios with expensive data communication, exchanging large neural network models becomes impractical. In such cases, prototype learning offers a viable solution by facilitating the exchange of only a few prototypes. Motivated by these considerations, our approach capitalizes on recent advancements in prototype learning, particularly the advantages offered by non-Euclidean geometries. In addition to presenting FedHP, we offer empirical evidence demonstrating its comparability to other state-of- the-art approaches while significantly reducing communication costs.
Petr Taborsky, Iacopo Colonnelli, Krzysztof Kurowski, Rakesh Sarma, Niels Henrik Pontoppidan, Branislav Jansík, Nicki Skafte Detlefsen, Jens Egholm Pedersen, Rasmus Larsen, Lars Kai Hansen
Towards a European AI Platform Miscellaneous
2nd EuroHPC User Day, 2024.
@misc{24:colonnelli:eurohpc-user-day,
title = {Towards a European AI Platform},
author = {Petr Taborsky and Iacopo Colonnelli and Krzysztof Kurowski and Rakesh Sarma and Niels Henrik Pontoppidan and Branislav Jansík and Nicki Skafte Detlefsen and Jens Egholm Pedersen and Rasmus Larsen and Lars Kai Hansen},
url = {https://datacloud.di.unito.it/index.php/s/nbk6YiABGsZXPp4},
year = {2024},
date = {2024-10-01},
address = {Amsterdam, Netherlands},
howpublished = {2nd EuroHPC User Day},
keywords = {ai},
pubstate = {published},
tppubtype = {misc}
}
Gianluca Mittone
Benchmarking HPC Performance for State-of-the-Art AI Workloads Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@misc{24:mittone:itadata:shpcpee,
title = {Benchmarking HPC Performance for State-of-the-Art AI Workloads},
author = {Gianluca Mittone},
url = {https://datacloud.di.unito.it/index.php/s/5Ep3W7cPW5baZfr},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
abstract = {Benchmarking the performance of modern High-Performance Computing (HPC) infrastructure on Artificial Intelligence (AI) workloads is a hot topic in the supercomputing community. While research communities and big-tech companies actively invest in larger, more powerful data centres to support AI research, the standard computational performance benchmarking tools (e.g., LINPACK) are increasingly becoming outdated since they are not specifically tailored for AI workloads. Some tools, such as MLPerf, are trying to bridge this gap, but the HPC community still has not adopted them as standards. Since this trend became particularly evident with the advent of Large Language Models (LLMs), this work will delve into LLM training at scale as a way to benchmark Top500 HPC infrastructures on current AI workloads. The scalability performances of a major LLM model (i.e., Meta's LLaMA) on different HPCs (Leonardo, LUMI, MeluXina, Karolina) are exposed and discussed along with their Top500 positioning.
However, it should be noted that state-of-the-art LLM models are not trained on thousands of computing nodes but on hundreds. This choice is due to multiple factors, such as the influence of the training scaling on the model's convergence and the instability of large-scale deployments due to hardware/software failure. A benchmarking approach based on the next-generation LLM training approach is proposed to bypass all these issues. State-of-the-art LLMs are not monolithic structures but Mixture-of-Experts (MoE) models; this design implies innovative frontiers for the distributed training of such models due to the experts' training being potentially more parallelisable than a single monolithic model. We thus propose to create an AI-oriented HPC benchmark suite based on the parallel training of MoE models to measure the throughput performance of HPC systems on state-of-the-art AI workloads.},
keywords = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {misc}
}
Benchmarking the performance of modern High-Performance Computing (HPC) infrastructure on Artificial Intelligence (AI) workloads is a hot topic in the supercomputing community. While research communities and big-tech companies actively invest in larger, more powerful data centres to support AI research, the standard computational performance benchmarking tools (e.g., LINPACK) are increasingly becoming outdated since they are not specifically tailored for AI workloads. Some tools, such as MLPerf, are trying to bridge this gap, but the HPC community still has not adopted them as standards. Since this trend became particularly evident with the advent of Large Language Models (LLMs), this work will delve into LLM training at scale as a way to benchmark Top500 HPC infrastructures on current AI workloads. The scalability performances of a major LLM model (i.e., Meta's LLaMA) on different HPCs (Leonardo, LUMI, MeluXina, Karolina) are exposed and discussed along with their Top500 positioning.
However, it should be noted that state-of-the-art LLM models are not trained on thousands of computing nodes but on hundreds. This choice is due to multiple factors, such as the influence of the training scaling on the model's convergence and the instability of large-scale deployments due to hardware/software failure. A benchmarking approach based on the next-generation LLM training approach is proposed to bypass all these issues. State-of-the-art LLMs are not monolithic structures but Mixture-of-Experts (MoE) models; this design implies innovative frontiers for the distributed training of such models due to the experts' training being potentially more parallelisable than a single monolithic model. We thus propose to create an AI-oriented HPC benchmark suite based on the parallel training of MoE models to measure the throughput performance of HPC systems on state-of-the-art AI workloads.
However, it should be noted that state-of-the-art LLM models are not trained on thousands of computing nodes but on hundreds. This choice is due to multiple factors, such as the influence of the training scaling on the model's convergence and the instability of large-scale deployments due to hardware/software failure. A benchmarking approach based on the next-generation LLM training approach is proposed to bypass all these issues. State-of-the-art LLMs are not monolithic structures but Mixture-of-Experts (MoE) models; this design implies innovative frontiers for the distributed training of such models due to the experts' training being potentially more parallelisable than a single monolithic model. We thus propose to create an AI-oriented HPC benchmark suite based on the parallel training of MoE models to measure the throughput performance of HPC systems on state-of-the-art AI workloads.
Gianluca Mittone
Pushing Federated Learning Boundaries: Three Innovative Distributed Intelligence Approaches Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: eupilot, fl, icsc
@misc{24:mittone:bighpc,
title = {Pushing Federated Learning Boundaries: Three Innovative Distributed Intelligence Approaches},
author = {Gianluca Mittone},
url = {https://datacloud.di.unito.it/index.php/s/eKbRtSAEdmSFJYW},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
abstract = {Federated learning is a distributed, privacy-preserving machine learning technique used on private, decentralised data. It allows multiple parties to cooperatively solve a common machine learning problem without sharing the local data. Three assumptions of state-of-the-art federated learning software constitute the starting points for this research work: 1) their inner workings being strictly tied to deep learning models, 2) the centralised structure currently implemented by many commercial frameworks, and 3) their assumption of being deployed on private, specialised computing infrastructures. The proposed research expands the federated learning paradigm to handle scenarios in which these three conditions do not hold. Such research problems are addressed methodologically and practically, and three open-source, proof-of-concept software are made freely available as tangible research results: OpenFL-x, FastFL, and xFFL.},
keywords = {eupilot, fl, icsc},
pubstate = {published},
tppubtype = {misc}
}
Federated learning is a distributed, privacy-preserving machine learning technique used on private, decentralised data. It allows multiple parties to cooperatively solve a common machine learning problem without sharing the local data. Three assumptions of state-of-the-art federated learning software constitute the starting points for this research work: 1) their inner workings being strictly tied to deep learning models, 2) the centralised structure currently implemented by many commercial frameworks, and 3) their assumption of being deployed on private, specialised computing infrastructures. The proposed research expands the federated learning paradigm to handle scenarios in which these three conditions do not hold. Such research problems are addressed methodologically and practically, and three open-source, proof-of-concept software are made freely available as tangible research results: OpenFL-x, FastFL, and xFFL.
Marco Edoardo Santimaria, Iacopo Colonnelli, Marco Aldinucci
Releasing the CAPIO middleware from MPI derived constraints Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: across, admire, capio, capiocl, eupex, icsc
@misc{24:santimaria:bighpc,
title = {Releasing the CAPIO middleware from MPI derived constraints},
author = {Marco Edoardo Santimaria and Iacopo Colonnelli and Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/zrJGD4i36fWdp5g},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
abstract = {CAPIO is a middleware that transparently injects streaming capabilities into file-based workflows. However, its implementation is limited to HPC environments based on the MPI framework, significantly limiting its applications. This paper will illustrate a proposed architecture and some preliminary results aimed at investigating the usage of a distributed files system as a communication media for the CAPIO middleware, with the ultimate goal of supporting both CLOUD-based and HPC-based workflows.},
keywords = {across, admire, capio, capiocl, eupex, icsc},
pubstate = {published},
tppubtype = {misc}
}
CAPIO is a middleware that transparently injects streaming capabilities into file-based workflows. However, its implementation is limited to HPC environments based on the MPI framework, significantly limiting its applications. This paper will illustrate a proposed architecture and some preliminary results aimed at investigating the usage of a distributed files system as a communication media for the CAPIO middleware, with the ultimate goal of supporting both CLOUD-based and HPC-based workflows.
Marco Edoardo Santimaria, Iacopo Colonnelli, Massimo Torquati, Marco Aldinucci
CAPIO: Cross Application Programamble IO Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: across, admire, capio, capiocl, eupex, icsc
@misc{24:santimaria:itadata:shpcpee,
title = {CAPIO: Cross Application Programamble IO},
author = {Marco Edoardo Santimaria and Iacopo Colonnelli and Massimo Torquati and Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/rg6LWwrZXi6tTXm},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
abstract = {With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to expand, further exacerbating quickly the performance gap between computing, memory, and storage technologies. CAPIO (Cross-Application Programmable I/O), is a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. In this presentation, we will introduce the CAPIO-CL language with its semantics, as well as the implementation of the CAPIO-CL language through the CAPIO middleware. We will also provide some case studies of how CAPIO has been employed to improve workflow execution time as well as some future directions.},
keywords = {across, admire, capio, capiocl, eupex, icsc},
pubstate = {published},
tppubtype = {misc}
}
With the increasing amount of digital data available for analysis and simulation, the class of I/O-intensive HPC workflows is fated to expand, further exacerbating quickly the performance gap between computing, memory, and storage technologies. CAPIO (Cross-Application Programmable I/O), is a middleware capable of injecting I/O streaming capabilities into file-based workflows, improving the computation-I/O overlap without the need to change the application code. In this presentation, we will introduce the CAPIO-CL language with its semantics, as well as the implementation of the CAPIO-CL language through the CAPIO middleware. We will also provide some case studies of how CAPIO has been employed to improve workflow execution time as well as some future directions.
Samuele Fonio Bruno Casella Oussama Harrak
Federated Adaboost for Survival Analysis Miscellaneous
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2nd Workshop on Advancements in Federated Learning (WAFL), 2024.
Abstract | Links | BibTeX | Tags: ai, epi, fl, icsc
@misc{23:ecmlpkdd:fedsurvboost,
title = {Federated Adaboost for Survival Analysis},
author = {Samuele Fonio Bruno Casella Oussama Harrak},
url = {https://datacloud.di.unito.it/index.php/s/DtXiQfne6BEC235},
year = {2024},
date = {2024-09-01},
address = {Vilnius, Lithuania},
abstract = {This work proposes FedSurvBoost, a federated learning pipeline for survival analysis based on the AdaBoost.F algorithm, which iteratively aggregates the best local weak hypotheses. Our method extends AdaBoost.F by removing the dependence on the number of classes coefficient from the computation of the weights of the best model. This makes it suitable for regression tasks, such as survival analysis. We show the effectiveness of our approach by comparing it with state-of-the-art methods, specifically developed for survival analysis problems, on two common survival datasets.},
howpublished = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2nd Workshop on Advancements in Federated Learning (WAFL)},
keywords = {ai, epi, fl, icsc},
pubstate = {published},
tppubtype = {misc}
}
This work proposes FedSurvBoost, a federated learning pipeline for survival analysis based on the AdaBoost.F algorithm, which iteratively aggregates the best local weak hypotheses. Our method extends AdaBoost.F by removing the dependence on the number of classes coefficient from the computation of the weights of the best model. This makes it suitable for regression tasks, such as survival analysis. We show the effectiveness of our approach by comparing it with state-of-the-art methods, specifically developed for survival analysis problems, on two common survival datasets.
Giulio Malenza
Exploiting C++ Parallel Algorithms through FastFlow Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: icsc
@misc{24:gmalenza:BigHPC2024,
title = {Exploiting C++ Parallel Algorithms through FastFlow},
author = {Giulio Malenza},
url = {https://datacloud.di.unito.it/index.php/s/GcpQ8cz9BRyM85B},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
abstract = {High-performance computing and artificial intelligent simulations necessitate the rapid processing of large quantities of data. To handle such data volumes efficiently, leveraging the parallelism inherent in algorithms is crucial. Consequently, parallel programming frameworks have been developed to fully exploit modern parallel architectures. Among these, C++ PSTL stands out for its user-friendliness, portability, and high performance.
In this study, we introduce a back-end for the PSTL implemented using the FastFlow parallel programming framework. We will evaluate correctness and performance of the back-end comparing results with other coming from traditional vendor-dependent back-ends like TBB and nvc++. Performance metrics are derived from running the LULESH application on both RISC-V and ARM architectures. Our results indicate that all three back-ends deliver comparable performance.},
keywords = {icsc},
pubstate = {published},
tppubtype = {misc}
}
High-performance computing and artificial intelligent simulations necessitate the rapid processing of large quantities of data. To handle such data volumes efficiently, leveraging the parallelism inherent in algorithms is crucial. Consequently, parallel programming frameworks have been developed to fully exploit modern parallel architectures. Among these, C++ PSTL stands out for its user-friendliness, portability, and high performance.
In this study, we introduce a back-end for the PSTL implemented using the FastFlow parallel programming framework. We will evaluate correctness and performance of the back-end comparing results with other coming from traditional vendor-dependent back-ends like TBB and nvc++. Performance metrics are derived from running the LULESH application on both RISC-V and ARM architectures. Our results indicate that all three back-ends deliver comparable performance.
In this study, we introduce a back-end for the PSTL implemented using the FastFlow parallel programming framework. We will evaluate correctness and performance of the back-end comparing results with other coming from traditional vendor-dependent back-ends like TBB and nvc++. Performance metrics are derived from running the LULESH application on both RISC-V and ARM architectures. Our results indicate that all three back-ends deliver comparable performance.
Giulio Malenza
Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: ai, DYMAN, icsc
@misc{24:gmalenza:scihpcexa,
title = {Exploring energy consumption of AI frameworks on a 64-core RV64 Server CPU},
author = {Giulio Malenza},
url = {https://datacloud.di.unito.it/index.php/s/5aTdyzNB6n9CREq},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
abstract = {In today's era of rapid technological advancement, artificial intelligence (AI) applications require large-scale, high-performance, and data-intensive computations, leading to significant energy demands. Addressing this challenge necessitates a combined approach involving both hardware and software innovations. Hardware manufacturers are developing new, efficient, and specialized solutions, with the RISC-V architecture emerging as a prominent player due to its open, extensible, and energy-efficient instruction set architecture (ISA). Simultaneously, software developers are creating new algorithms and frameworks,
yet their energy efficiency often remains unclear.
In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. Specifically, we examine the energy consumption of deep learning inference models across various AI frameworks. By comparing the performance of different frameworks, we aim to provide a detailed understanding of how these frameworks can optimize energy consumption on this architecture.},
keywords = {ai, DYMAN, icsc},
pubstate = {published},
tppubtype = {misc}
}
In today's era of rapid technological advancement, artificial intelligence (AI) applications require large-scale, high-performance, and data-intensive computations, leading to significant energy demands. Addressing this challenge necessitates a combined approach involving both hardware and software innovations. Hardware manufacturers are developing new, efficient, and specialized solutions, with the RISC-V architecture emerging as a prominent player due to its open, extensible, and energy-efficient instruction set architecture (ISA). Simultaneously, software developers are creating new algorithms and frameworks,
yet their energy efficiency often remains unclear.
In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. Specifically, we examine the energy consumption of deep learning inference models across various AI frameworks. By comparing the performance of different frameworks, we aim to provide a detailed understanding of how these frameworks can optimize energy consumption on this architecture.
yet their energy efficiency often remains unclear.
In this study, we conduct a comprehensive benchmark analysis of machine learning (ML) applications on the 64-core SOPHON SG2042 RISC-V architecture. Specifically, we examine the energy consumption of deep learning inference models across various AI frameworks. By comparing the performance of different frameworks, we aim to provide a detailed understanding of how these frameworks can optimize energy consumption on this architecture.
Iacopo Colonnelli
Scientific Workflows in the Heterogeneous Computing Era Miscellaneous
2024.
@misc{24:icolonne:ICSC,
title = {Scientific Workflows in the Heterogeneous Computing Era},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/CyxiWsDbdg6rbpQ},
year = {2024},
date = {2024-09-01},
address = {Roma, Italy},
keywords = {icsc},
pubstate = {published},
tppubtype = {misc}
}
Lorenzo Brescia
Performance Analysis on DNA Alignment Workload with Intel SGX Multithreading Miscellaneous
Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024, 2024.
Links | BibTeX | Tags: confidential, icsc
@misc{24:brescia:bigHPC:talk,
title = {Performance Analysis on DNA Alignment Workload with Intel SGX Multithreading},
author = {Lorenzo Brescia},
url = {https://datacloud.di.unito.it/index.php/s/wqgYiKpHBw5zbSa},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
howpublished = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {misc}
}
Adriano Marques Garcia
Assessing Large Language Models Inference Performance on a 64-core RISC-V CPU with Silicon-Enabled Vectors Miscellaneous
Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024, 2024.
Links | BibTeX | Tags: confidential, icsc
@misc{24:garcia:bigHPC:talk,
title = {Assessing Large Language Models Inference Performance on a 64-core RISC-V CPU with Silicon-Enabled Vectors},
author = {Adriano Marques Garcia},
url = {https://datacloud.di.unito.it/index.php/s/eN6Z62RQr2QsRYa},
year = {2024},
date = {2024-09-01},
address = {Pisa, Italy},
howpublished = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {misc}
}
Iacopo Colonnelli
Scientific Workflows in the Continuum Era Miscellaneous
2024, (Keynote Talk).
Abstract | Links | BibTeX | Tags: icsc
@misc{24:icolonne:wscc,
title = {Scientific Workflows in the Continuum Era},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/PkqYA3p38XLKgrt},
year = {2024},
date = {2024-08-01},
address = {Madrid, Spain},
abstract = {Thanks to their generality, workflow models represent a powerful abstraction for designing complex applications and executing them on large-scale distributed architectures. However, several additional challenges appear when transitioning from cloud/HPC environments to the entire compute continuum. Continuum execution environments are fully distributed and modular, and modules can be heterogeneous and independent of each other. In addition, continuum workflows often rely on multiple intercommunicating agents that form complex micro-services architectures. Different agents deal with different communication and parallelization paradigms: network-based stream processing at the edge and file-based batch processing on HPC facilities. Finally, support for efficient interactive workflows in the continuum remains an open research problem. This talk explores these challenges and provides insights on how to deal with them. A ready-to-use software library accompanies each proposed solution to facilitate the reproducibility and reusability of the presented concepts.},
note = {Keynote Talk},
keywords = {icsc},
pubstate = {published},
tppubtype = {misc}
}
Thanks to their generality, workflow models represent a powerful abstraction for designing complex applications and executing them on large-scale distributed architectures. However, several additional challenges appear when transitioning from cloud/HPC environments to the entire compute continuum. Continuum execution environments are fully distributed and modular, and modules can be heterogeneous and independent of each other. In addition, continuum workflows often rely on multiple intercommunicating agents that form complex micro-services architectures. Different agents deal with different communication and parallelization paradigms: network-based stream processing at the edge and file-based batch processing on HPC facilities. Finally, support for efficient interactive workflows in the continuum remains an open research problem. This talk explores these challenges and provides insights on how to deal with them. A ready-to-use software library accompanies each proposed solution to facilitate the reproducibility and reusability of the presented concepts.
Alberto Mulone, Doriana Medić, Marco Aldinucci
A Fault Tolerance mechanism for Hybrid Scientific Workflows Miscellaneous
1st workshop about High-Performance e-Science (HiPES), 2024.
Abstract | Links | BibTeX | Tags: eupex, icsc, streamflow
@misc{24:madrid:hipes:talk,
title = {A Fault Tolerance mechanism for Hybrid Scientific Workflows},
author = {Alberto Mulone and Doriana Medić and Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/9Ddj6fGgmDbLDXj},
year = {2024},
date = {2024-08-01},
address = {Madrid, Spain},
abstract = {In large distributed systems, failures are a daily event occurring frequently, especially with growing numbers of computation tasks and locations on which they are deployed. The advantage of representing an application as a workflow is possibility to utilize the Workflow Management Systems which are reliable systems guaranteeing the correct execution of the application and providing the features such as portability, scalability, and fault tolerance. Over recent years, the emergence of hybrid workflows has posed new and intriguing challenges by increasing the possibility of distributing computations involving heterogeneous and independent environments. As a consequence, the number of possible points of failure in the execution augmented, creating different important challenges interesting to study.},
howpublished = {1st workshop about High-Performance e-Science (HiPES)},
keywords = {eupex, icsc, streamflow},
pubstate = {published},
tppubtype = {misc}
}
In large distributed systems, failures are a daily event occurring frequently, especially with growing numbers of computation tasks and locations on which they are deployed. The advantage of representing an application as a workflow is possibility to utilize the Workflow Management Systems which are reliable systems guaranteeing the correct execution of the application and providing the features such as portability, scalability, and fault tolerance. Over recent years, the emergence of hybrid workflows has posed new and intriguing challenges by increasing the possibility of distributing computations involving heterogeneous and independent environments. As a consequence, the number of possible points of failure in the execution augmented, creating different important challenges interesting to study.
Marco Edoardo Santimaria
CAPIO-CL: Cross Application Programmable IO - Coordination Language Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: across, admire, capio, eupex, icsc
@misc{24:santimaria:hlpp:capiocl,
title = {CAPIO-CL: Cross Application Programmable IO - Coordination Language},
author = {Marco Edoardo Santimaria},
url = {https://datacloud.di.unito.it/index.php/s/zsKY3PWzX5NFCiX},
year = {2024},
date = {2024-07-01},
address = {Pisa, Italy},
abstract = {The performance bottleneck in file-based workflows remains a pressing issue in the realm of I/O-based workflows. To address this challenge, a novel annotation language has been developed. CAPIO-CL is positioned as an innovative I/O coordination language, enabling users to annotate data dependencies within file-based workflows with synchronization semantics pertinent to the involved files and directories. Through the information provided by the language, optimization opportunities arise in streaming and preemptive data movement. This paper serves to illustrate the semantics and syntax enabling CAPIO-CL to enhance the performance of in situ workflows without necessitating the rewriting or modification of the original workflow application steps. Finally, an analysis of CAPIO-CL is provided, taking into consideration both language expressiveness and application performance enhancement.},
keywords = {across, admire, capio, eupex, icsc},
pubstate = {published},
tppubtype = {misc}
}
The performance bottleneck in file-based workflows remains a pressing issue in the realm of I/O-based workflows. To address this challenge, a novel annotation language has been developed. CAPIO-CL is positioned as an innovative I/O coordination language, enabling users to annotate data dependencies within file-based workflows with synchronization semantics pertinent to the involved files and directories. Through the information provided by the language, optimization opportunities arise in streaming and preemptive data movement. This paper serves to illustrate the semantics and syntax enabling CAPIO-CL to enhance the performance of in situ workflows without necessitating the rewriting or modification of the original workflow application steps. Finally, an analysis of CAPIO-CL is provided, taking into consideration both language expressiveness and application performance enhancement.
Giulio Malenza
Preliminary analysis of model parallelism applications on a 64-core RV64 Server CPU Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: eupilot, icsc
@misc{24:gmalenza:hlpp:MPRISC-v,
title = {Preliminary analysis of model parallelism applications on a 64-core RV64 Server CPU},
author = {Giulio Malenza},
url = {https://datacloud.di.unito.it/index.php/s/JrWwKALeaFEJSQo},
year = {2024},
date = {2024-07-01},
address = {Pisa, Italy},
abstract = {Massive Data Parallel workloads, driven by inference on large ML models, are pushing hardware vendors to develop efficient and cost-effective multi-core server CPUs. The RISC-V architecture plays a prominent role due to its open, extensible and energy-friendly ISA. Despite significant progress in recent years, finding efficient methods to run parallel applications on new architectures to harness their maximum performance fully remains a challenge. In this study, we benchmark the inference of machine learning models on the SOPHON SG2042 SoC, the first server-grade CPU based on the RV64 ISA, composed of 64 cores arranged in a grid of 16 groups of 4 cores. Specifically, we aim to enhance performance via better cache hit ratios stemming from model parallelism to split and assign parts of the model to specific (groups of) cores using a pipeline execution. We orchestrate execution using FastFlow, a low-level programming framework designed for multithreaded streaming applications. By comparing the results against the standard multi-core inference and analyzing the effects of different submodel-to-core mapping strategies, we aim to provide a comprehensive understanding of how the model parallel approach can maximize efficiency and utilization of hardware resources.},
keywords = {eupilot, icsc},
pubstate = {published},
tppubtype = {misc}
}
Massive Data Parallel workloads, driven by inference on large ML models, are pushing hardware vendors to develop efficient and cost-effective multi-core server CPUs. The RISC-V architecture plays a prominent role due to its open, extensible and energy-friendly ISA. Despite significant progress in recent years, finding efficient methods to run parallel applications on new architectures to harness their maximum performance fully remains a challenge. In this study, we benchmark the inference of machine learning models on the SOPHON SG2042 SoC, the first server-grade CPU based on the RV64 ISA, composed of 64 cores arranged in a grid of 16 groups of 4 cores. Specifically, we aim to enhance performance via better cache hit ratios stemming from model parallelism to split and assign parts of the model to specific (groups of) cores using a pipeline execution. We orchestrate execution using FastFlow, a low-level programming framework designed for multithreaded streaming applications. By comparing the results against the standard multi-core inference and analyzing the effects of different submodel-to-core mapping strategies, we aim to provide a comprehensive understanding of how the model parallel approach can maximize efficiency and utilization of hardware resources.
Marco Aldinucci
From HPC4AI to Software & Integration living lab to innovation Miscellaneous
HPC as an enabling platform for AI event, 2024.
Abstract | Links | BibTeX | Tags: ai, HPC, icsc
@misc{24:ma:hpcai:talk,
title = {From HPC4AI to Software & Integration living lab to innovation},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/3SS3Xa9XorN6D9o},
year = {2024},
date = {2024-06-01},
address = {Torino, Italy},
abstract = {The talk presents the motivation and the activity of the "Software and Integration" lab at UNITO.},
howpublished = {HPC as an enabling platform for AI event},
keywords = {ai, HPC, icsc},
pubstate = {published},
tppubtype = {misc}
}
The talk presents the motivation and the activity of the "Software and Integration" lab at UNITO.
Marco Aldinucci
Software & Integration lab of FutureHPC spoke Miscellaneous
HPC as an enabling platform for AI event, 2024.
Abstract | Links | BibTeX | Tags: ai, HPC, icsc
@misc{24:ma:swi:lab,
title = {Software & Integration lab of FutureHPC spoke},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/WDjyXCGyYFJDQSd},
year = {2024},
date = {2024-06-01},
address = {Torino, Italy},
abstract = {The presentation describes the main the activity of the "Software and Integration" lab at UNITO across its main flagship codes.},
howpublished = {HPC as an enabling platform for AI event},
keywords = {ai, HPC, icsc},
pubstate = {published},
tppubtype = {misc}
}
The presentation describes the main the activity of the "Software and Integration" lab at UNITO across its main flagship codes.
Iacopo Colonnelli, Robert Birke, Giulio Malenza, Gianluca Mittone, Alberto Mulone, Marco Aldinucci
Cross-Facility Federated Learning - Part II Miscellaneous
2024, (Invited talk).
Links | BibTeX | Tags: eupex, icsc, space
@misc{24:ic:elise:xffl,
title = {Cross-Facility Federated Learning - Part II},
author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/7HonBpcWPxotXLX},
year = {2024},
date = {2024-06-01},
address = {Helsinki, Finland},
note = {Invited talk},
keywords = {eupex, icsc, space},
pubstate = {published},
tppubtype = {misc}
}
Lorenzo Brescia
Towards Secure WMS with TEEs Miscellaneous
Mentoring session: HPC Summer School, university of Trento, 2024.
Links | BibTeX | Tags: confidential, icsc
@misc{24:brescia:hpc:school:talk,
title = {Towards Secure WMS with TEEs},
author = {Lorenzo Brescia},
url = {https://datacloud.di.unito.it/index.php/s/eAxEgqiTsGSRQz4},
year = {2024},
date = {2024-06-01},
address = {Trento, Italy},
howpublished = {Mentoring session: HPC Summer School, university of Trento},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {misc}
}
Iacopo Colonnelli
CWL Working Groups Miscellaneous
2024 CWL Conference, 2024.
Abstract | Links | BibTeX | Tags: icsc
@misc{24:icolonne:cwlcon2024,
title = {CWL Working Groups},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/zZDKdL8deLd4jSi},
year = {2024},
date = {2024-05-01},
address = {Amsterdam, Netherlands},
abstract = {This presentation introduces the new CWL Working Groups initiative, describing what a Working Group actually is, which Working Groups already exist in the CWL community, and how anybody can create a new officially recognized Working Group. Then, the presentation will explore the CWL4HPC Working Group, using it as an example of how a CWL Working Group can actually work.},
howpublished = {2024 CWL Conference},
keywords = {icsc},
pubstate = {published},
tppubtype = {misc}
}
This presentation introduces the new CWL Working Groups initiative, describing what a Working Group actually is, which Working Groups already exist in the CWL community, and how anybody can create a new officially recognized Working Group. Then, the presentation will explore the CWL4HPC Working Group, using it as an example of how a CWL Working Group can actually work.
Gianluca Mittone
Into to Federated Learning Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: fl, icsc
@misc{24:mittone:ictp,
title = {Into to Federated Learning},
author = {Gianluca Mittone},
url = {https://datacloud.di.unito.it/index.php/s/nSwwmedjqe2jbWJ},
year = {2024},
date = {2024-05-01},
address = {Trieste, Italy},
abstract = {Machine Learning (ML) is the branch of Artificial Intelligence focused on developing algorithms capable of adapting and improving their predictive or generative performance by feeding on data. Adapting or improving the system’s behaviour based on the provided data is called learning since it is similar to the human learning process in many aspects. The same ML algorithm, usually referred to as a model, trained on different data will thus expose different capabilities and can, therefore, solve different tasks. FL is a relatively recent distributed ML methodology aiming to bridge the gap between the need to train ever bigger ML models on ever larger datasets and the individual and companies’ will to protect and not share their private data. From another point of view, FL is also a way to distribute the training of an ML model even more than before. However, it should be considered that the learning performance of FL is usually lower than that of traditional centralised learning. This course will start from Kairouz ad McMahan’s definition of FL: ”Federated learning is a machine learning setting where multiple entities (clients) collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.” From this starting point, the most significant aspects of FL will be exposed and discussed. This tutorial will particularly explore FL from both the learning and computational [5] performance perspectives, investigating its pros and cons in a distributed ML setting. Since FL natively targets data privacy, some insights on how the FL process can be attacked and protected will also be discussed from a high-level perspective. Finally, a hands-on session will guide the participants in building a basic FL system, providing a better understanding of the major implementational difficulties of such a technique.},
keywords = {fl, icsc},
pubstate = {published},
tppubtype = {misc}
}
Machine Learning (ML) is the branch of Artificial Intelligence focused on developing algorithms capable of adapting and improving their predictive or generative performance by feeding on data. Adapting or improving the system’s behaviour based on the provided data is called learning since it is similar to the human learning process in many aspects. The same ML algorithm, usually referred to as a model, trained on different data will thus expose different capabilities and can, therefore, solve different tasks. FL is a relatively recent distributed ML methodology aiming to bridge the gap between the need to train ever bigger ML models on ever larger datasets and the individual and companies’ will to protect and not share their private data. From another point of view, FL is also a way to distribute the training of an ML model even more than before. However, it should be considered that the learning performance of FL is usually lower than that of traditional centralised learning. This course will start from Kairouz ad McMahan’s definition of FL: ”Federated learning is a machine learning setting where multiple entities (clients) collaborate in solving a machine learning problem, under the coordination of a central server or service provider. Each client’s raw data is stored locally and not exchanged or transferred; instead, focused updates intended for immediate aggregation are used to achieve the learning objective.” From this starting point, the most significant aspects of FL will be exposed and discussed. This tutorial will particularly explore FL from both the learning and computational [5] performance perspectives, investigating its pros and cons in a distributed ML setting. Since FL natively targets data privacy, some insights on how the FL process can be attacked and protected will also be discussed from a high-level perspective. Finally, a hands-on session will guide the participants in building a basic FL system, providing a better understanding of the major implementational difficulties of such a technique.
Iacopo Colonnelli
Dynamic hybrid workflows for Deep Learning on HPC infrastructure Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: icsc, jupyter-workflow, streamflow
@misc{24:icolonne:ictp,
title = {Dynamic hybrid workflows for Deep Learning on HPC infrastructure},
author = {Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/EaFHJEKNbW5oXeq},
year = {2024},
date = {2024-05-01},
address = {Trieste, Italy},
abstract = {Hybrid workflow abstractions allow users to quickly design and orchestrate cross-facility workloads, decoupling tasks from environment-specific technical details to reduce complexity and increase reusability. Plus, workflow descriptions help ensure the reproducibility of scientific experiments through prospective and retrospective provenance collection. This module has been designed to provide a hands-on exploration of scientific workflows from various angles, from the initial design phase to their orchestration at extreme scales. We will use the practical example of the CommonWorkflow Language (CWL) open standard to demonstrate how workflows can be written, and the StreamFlow workflow system to execute them seamlessly on the CINECA HPC facility. We will also delve into the integration between scientific workflows and Jupyter Notebooks, which aims to give data scientists a familiar interface to scientific workflows. In this module, students will gain a comprehensive understanding of scientific workflows. They will learn how to use these workflows to model and orchestrate Machine Learning and Deep Learning pipelines. Additionally, they will explore how modern workflow management systems can efficiently scale data-oriented workloads from a researcher’s laptop to an entire HPC facility.},
keywords = {icsc, jupyter-workflow, streamflow},
pubstate = {published},
tppubtype = {misc}
}
Hybrid workflow abstractions allow users to quickly design and orchestrate cross-facility workloads, decoupling tasks from environment-specific technical details to reduce complexity and increase reusability. Plus, workflow descriptions help ensure the reproducibility of scientific experiments through prospective and retrospective provenance collection. This module has been designed to provide a hands-on exploration of scientific workflows from various angles, from the initial design phase to their orchestration at extreme scales. We will use the practical example of the CommonWorkflow Language (CWL) open standard to demonstrate how workflows can be written, and the StreamFlow workflow system to execute them seamlessly on the CINECA HPC facility. We will also delve into the integration between scientific workflows and Jupyter Notebooks, which aims to give data scientists a familiar interface to scientific workflows. In this module, students will gain a comprehensive understanding of scientific workflows. They will learn how to use these workflows to model and orchestrate Machine Learning and Deep Learning pipelines. Additionally, they will explore how modern workflow management systems can efficiently scale data-oriented workloads from a researcher’s laptop to an entire HPC facility.
Iacopo Colonnelli
CWL in the HPC Ecosystem Miscellaneous
Workshop on workflow languages for HEP analysis, 2024.
Links | BibTeX | Tags: across, eupex, icsc, space, streamflow
@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}
}
Alberto Mulone
Workflows for future High-Performance Computing Miscellaneous
COMETE PhD Workshop, 2024.
@misc{24:amulone:comete,
title = {Workflows for future High-Performance Computing},
author = {Alberto Mulone},
url = {https://datacloud.di.unito.it/index.php/s/ZGG8fLMp5B7qRHS},
year = {2024},
date = {2024-04-01},
address = {Torino, Italy},
howpublished = {COMETE PhD Workshop},
keywords = {icsc},
pubstate = {published},
tppubtype = {misc}
}
Lorenzo Brescia
Secure Generic Remote Workflow Execution with TEEs Miscellaneous
Proceedings of the 2nd Workshop on Workflows in Distributed Environments, 2024.
Abstract | Links | BibTeX | Tags: confidential, icsc
@misc{23:brescia:wide:talk,
title = {Secure Generic Remote Workflow Execution with TEEs},
author = {Lorenzo Brescia},
url = {https://datacloud.di.unito.it/index.php/s/Prxq6EWGbcN8sWx},
year = {2024},
date = {2024-04-01},
address = {Athens, Greece},
abstract = {In scientific environments, the frequent need to process substantial volumes of data poses a common challenge. Individuals tasked with executing these computations frequently encounter a deficit in local computational resources, leading them to opt for the facilities of a Cloud Service Provider (CSP) for data processing. However, the data subjected to these calculations may be subject to confidentiality constraints. This paper introduces a proof-of-concept framework that leverages Gramine LibOS and Intel SGX, enabling the protection of generic remote workflow computations through SGX enclaves as Trusted Execution Environments (TEEs). The framework entails the delineation of user and CSP behavior and has been implemented using Bash scripts. Furthermore, an infrastructure has been designed for the Data Center Attestation Primitives (DCAP) remote attestation mechanism, wherein the user gains trust in the proper instantiation of the enclave within the CSP. To assess the framework efficacy, it has been tested on two distinct workflows, one trivial and the other involving real-world bioinformatics applications for processing DNA data. The performance study revealed that the framework incurred an acceptable overhead, ranging from a factor of x1.4 to x1.8 compared to unsafe execution practice.},
howpublished = {Proceedings of the 2nd Workshop on Workflows in Distributed Environments},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {misc}
}
In scientific environments, the frequent need to process substantial volumes of data poses a common challenge. Individuals tasked with executing these computations frequently encounter a deficit in local computational resources, leading them to opt for the facilities of a Cloud Service Provider (CSP) for data processing. However, the data subjected to these calculations may be subject to confidentiality constraints. This paper introduces a proof-of-concept framework that leverages Gramine LibOS and Intel SGX, enabling the protection of generic remote workflow computations through SGX enclaves as Trusted Execution Environments (TEEs). The framework entails the delineation of user and CSP behavior and has been implemented using Bash scripts. Furthermore, an infrastructure has been designed for the Data Center Attestation Primitives (DCAP) remote attestation mechanism, wherein the user gains trust in the proper instantiation of the enclave within the CSP. To assess the framework efficacy, it has been tested on two distinct workflows, one trivial and the other involving real-world bioinformatics applications for processing DNA data. The performance study revealed that the framework incurred an acceptable overhead, ranging from a factor of x1.4 to x1.8 compared to unsafe execution practice.
Giulio Malenza, Marco Edoardo Santimaria
Benchmarking Parallelization Models through Karmarkar`s algorithm Miscellaneous
2024.
Abstract | Links | BibTeX | Tags: HPC, icsc
@misc{24:pdp:karmarkartalk,
title = {Benchmarking Parallelization Models through Karmarkar`s algorithm},
author = {Giulio Malenza and Marco Edoardo Santimaria},
url = {https://datacloud.di.unito.it/index.php/s/JjKcAJpYS7ctX9r},
year = {2024},
date = {2024-03-01},
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}
}
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
Robert Birke
FLaaS: Federated Learning as a Service Miscellaneous
ICSC - Spoke 1 meeting, 2024.
Abstract | Links | BibTeX | Tags: ai, icsc
@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 = {ai, icsc},
pubstate = {published},
tppubtype = {misc}
}
Presentation about the Innovation Grant in collaboration with IFAB
Alberto Mulone
Cross-Platform Full Waveform Inversion Miscellaneous
ICSC - Spoke 1 meeting, 2024.
Abstract | Links | BibTeX | Tags: icsc, streamflow
@misc{24:icsc:spoke1:eni,
title = {Cross-Platform Full Waveform Inversion},
author = {Alberto Mulone},
url = {https://datacloud.di.unito.it/index.php/s/M3HkxA5wsBPS5ro},
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}
}
Presentation about the Innovation Grant in collaboration with ENI
Robert Birke
The impact of the advances in generative models on applications and systems Miscellaneous
8th GDR RSD / ASF Winter School on Distributed Systems & Networks 2024, 2024, (Keynote talk).
Abstract | Links | BibTeX | Tags: ai, eupilot, textarossa
@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}
}
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.
Gianluca Mittone
RISC-V for AI Miscellaneous
High Performance, Edge And Cloud computing Conference 2024 (HiPEAC 2024), 2024.
Abstract | Links | BibTeX | Tags: eupilot, icsc
@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}
}
AI-focused RISC-V-based hardware accelerators
2023
Barbara Cantalupo
The Software Heritage Analytics Framework Miscellaneous
Admire User Meeting, 2023, 2023.
Abstract | Links | BibTeX | Tags: admire
@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}
}
A framework for analytics on top of Software Heritage
Simone Pernice
GreatNector Modelling Tool Miscellaneous
Admire User Meeting, 2023, 2023.
Abstract | Links | BibTeX | Tags: admire
@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}
}
A tool for system modelling I/O queue behaviour
Marco Aldinucci
Federated Learning: A Distributed System Viewpoint Miscellaneous
Bicocca University seminars, Milan, Italy, 2023, (Invited talk).
Abstract | Links | BibTeX | Tags: eupilot, icsc, textarossa
@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}
}
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.
Iacopo Colonnelli, Robert Birke, Giulio Malenza, Gianluca Mittone, Alberto Mulone, Marco Aldinucci, Valerio Basile, Marco Antonio Stranisci, Viviana Patti, Jeroen Galjaard, Lydia Y. Chen, Sanzio Bassini, Massimiliano Guarrasi, Gabriella Scipione, Jan Martinovič, Vit Vondrák
Cross-Facility Federated Learning Miscellaneous
1st EuroHPC User Day, 2023.
Links | BibTeX | Tags: across, ai, eupex, eupilot, HPC
@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, ai, eupex, eupilot, HPC},
pubstate = {published},
tppubtype = {misc}
}
Gianluca Mittone, Giulio Malenza, Marco Aldinucci, Robert Birke
Distributed Edge Inference: an Experimental Study on Multiview Detection Miscellaneous
The 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2023), 2023.
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@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 = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {misc}
}
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.
Lorenzo Brescia, Iacopo Colonnelli
Trusted Computing at Scale Miscellaneous
CN HPC Flagship 4 Working Day, 2023.
Links | BibTeX | Tags: confidential, icsc
@misc{23:brescia:trusted:workflow:fl4:talk,
title = {Trusted Computing at Scale},
author = {Lorenzo Brescia and Iacopo Colonnelli},
url = {https://datacloud.di.unito.it/index.php/s/5ij6tLd5SAX4Nn4},
year = {2023},
date = {2023-12-01},
address = {Turin, Italy},
howpublished = {CN HPC Flagship 4 Working Day},
keywords = {confidential, icsc},
pubstate = {published},
tppubtype = {misc}
}
Alberto Scionti, Iacopo Colonnelli
Orchestrating Multi-Domain Workflows: The ACROSS Approach Miscellaneous
Workflows Community: Modern Workflows for Continuum and Cross-Facility Computing, 2023.
Links | BibTeX | Tags: across, streamflow
@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}
}
Marco Aldinucci, Elena Baralis, Valeria Cardellini, Iacopo Colonnelli, Marco Danelutto, Sergio Decherchi, Giuseppe Di Modica, Luca Ferrucci, Marco Gribaudo, Francesco Iannone, Marco Lapegna, Doriana Medić, Giuseppa Muscianisi, Francesca Righetti, Eva Sciacca, Nicola Tonellotto, Mauro Tortonesi, Paolo Trunfio, Tullio Vardanega
A Systematic Mapping Study of Italian Research on Workflows Miscellaneous
18th Workshop on Workflows in Support of Large-Scale Science (WORKS 2023), 2023.
Abstract | Links | BibTeX | Tags: icsc
@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}
}
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.
Iacopo Colonnelli
ACROSS: HPC Big Data Artificial Intelligence Cross Stack Platform Towards Exascale Miscellaneous
LN HPC-KTT Assemblea Nazionale 2023, 2023.
Links | BibTeX | Tags: across, streamflow
@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}
}
Iacopo Colonnelli, Doriana Medić, Barbara Cantalupo, Marco Aldinucci
Università degli Studi di Torino: Alpha parallel research group Miscellaneous
HaMMon Kick-Off meeting, 2023.
Links | BibTeX | Tags: icsc, streamflow
@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}
}
Giulio Malenza, Valentina Cesare, Marco Aldinucci
Performance portability in HPC: the Gaia use-case. Miscellaneous
2nd Italian Conference on Big Data and Data Science (ITADATA 2023), 2023.
@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}
}
Iacopo Colonnelli
Workflow models for heterogeneous distributed systems Miscellaneous
2nd Italian Conference on Big Data and Data Science (ITADATA 2023), 2023, (Best PhD Thesis Award).
Links | BibTeX | Tags: jupyter-workflow, streamflow
@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}
}
Samuele Fonio
Benchmarking Federated Learning Frameworks for Medical Imaging Tasks Miscellaneous
Image Analysis and Processing - ICIAP 2023 - 22th International Conference - FedMed, 2023.
Abstract | Links | BibTeX | Tags: ai, eupilot, fl, icsc
@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 = {ai, eupilot, fl, icsc},
pubstate = {published},
tppubtype = {misc}
}
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.
Gianluca Mittone, Samuele Fonio
Benchmarking Federated Learning Scalability Miscellaneous
2nd Italian Conference on Big Data and Data Science (ITADATA 2023), 2023.
Abstract | Links | BibTeX | Tags: ai, eupilot, fl, icsc
@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 = {ai, eupilot, fl, icsc},
pubstate = {published},
tppubtype = {misc}
}
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.
Gianluca Mittone, Walter Riviera, Iacopo Colonnelli, Robert Birke, Marco Aldinucci
Model-Agnostic Federated Learning Miscellaneous
29th International European Conference on Parallel and Distributed Computing (Euro-Par '23), 2023.
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@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 = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {misc}
}
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.
Gianluca Mittone, Robert Birke, Marco Aldinucci
Model-Agnostic Federated Learning Miscellaneous
29th International European Conference on Parallel and Distributed Computing (Euro-Par '23), 2023.
Abstract | Links | BibTeX | Tags: eupilot, icsc
@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}
}
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.
Giulio Malenza
Building an accelerated OpenFOAM Proof-of-Concept application using Modern C++. Miscellaneous
18th OpenFOAM Workshop 2023, Genova, 2023.
@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}
}
Marco Aldinucci, Rossana Damiano
OpenScience@UNITO: Milestone 1: a novel continuous training course for data stewards Miscellaneous
University of Torino research commission, 2023, (OpenScience group of the University of Torino).
Links | BibTeX | Tags: openscience
@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}
}
Alberto Mulone, Sherine Awad, Davide Chiarugi, Marco Aldinucci
Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment Miscellaneous
47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, 2023.
Abstract | Links | BibTeX | Tags: across, icsc, streamflow
@misc{23:mulone:wide:talk,
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},
url = {https://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy},
year = {2023},
date = {2023-06-01},
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}
}
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.
Iacopo Colonnelli
Workflows and the Common Workflow Language (CWL) Miscellaneous
OSA2Micro: An Open Science Approach to Microbiology data integration, 2023, (Invited talk).
Links | BibTeX | Tags: streamflow
@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}
}
Iacopo Colonnelli
UNITO tools presentation Miscellaneous
CN HPC Flagship 3 Working Day, 2023.
Links | BibTeX | Tags: jupyter-workflow, streamflow
@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}
}
Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medić, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning Miscellaneous
20th ACM international conference on computing frontiers (CF '23), 2023, (Invited talk).
Abstract | Links | BibTeX | Tags: ai, eupilot, icsc
@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 = {ai, eupilot, icsc},
pubstate = {published},
tppubtype = {misc}
}
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.
Gianluca Mittone, Filip Svoboda, Marco Aldinucci, Nicholas D. Lane, Pietro Lio'
A Federated Learning Benchmark for Drug-Target Interaction Miscellaneous
2023 ACM international Web Conference (WWW '23), 2023, (Invited talk).
Abstract | Links | BibTeX | Tags: eupilot, icsc
@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}
}
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.
Sofia Karvounari, Eleni Mathioulaki, Michael R. Crusoe, Iacopo Colonnelli
Standardised Workflows at EBRAINS Miscellaneous
Human Brain Project Summit 2023, 2023, (Invited talk).
Abstract | Links | BibTeX | Tags: across, eupex, space, streamflow
@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}
}
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.
Marco Aldinucci
Experimenting with Systems for Decentralized Machine Learning Miscellaneous
NVidia GTC 2023, 2023.
Abstract | Links | BibTeX | Tags: across, admire, epi, eumaster4hpc, eupex, eupilot, hpc4ai, space, textarossa
@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}
}
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.
Marco Aldinucci
HPC4AI: The Research on AI beyond the public cloud Miscellaneous
CENTAI kick-off meeting, 2023.
Links | BibTeX | Tags: across, admire, brainteaser, epi, eumaster4hpc, eupex, eupilot, hpc4ai, space, textarossa
@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}
}
Iacopo Colonnelli
CWL for HPC: are we there yet? Miscellaneous
2023 CWL Conference, 2023, (Invited talk).
Abstract | Links | BibTeX | Tags: across, eupex, streamflow
@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}
}
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.
Marco Aldinucci
From HPC4AI to ICSC living lab: Where systems are the research Miscellaneous
Dell Advanced Computing Workshop 2023: HPC and Beyond, 2023.
Links | BibTeX | Tags: admire, eupex, eupilot, hpc4ai, textarossa
@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}
}
Gianluca Mittone
Paving the way to innovative tools for Federated Learning Miscellaneous
2023 HiPEAC Conference, 2023, (Invited talk).
Abstract | Links | BibTeX | Tags: eupilot
@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}
}
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.
Bruno Casella, Samuele Fonio
Architecture-Based FedAvg for Vertical Federated Learning Miscellaneous
2023.
Abstract | Links | BibTeX | Tags: ai, epi, fl, icsc
@misc{23:casella:architecturalfedavgtalk,
title = {Architecture-Based FedAvg for Vertical Federated Learning},
author = {Bruno Casella and Samuele Fonio},
url = {https://datacloud.di.unito.it/index.php/s/kJQxnqG4d2ZSicK},
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.},
keywords = {ai, epi, fl, icsc},
pubstate = {published},
tppubtype = {misc}
}
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.
Doriana Medić, Marco Aldinucci
Towards formal model for location aware workflows Miscellaneous
2023.
Abstract | Links | BibTeX | Tags: eupex, icsc
@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}
}
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.
2022
Iacopo Colonnelli, Barbara Cantalupo, Doriana Medić, Marco Aldinucci
Hybrid workflows for heterogeneous distributed computing Miscellaneous
3rd Italian Workshop on HPC (ITWSHPC), 2022.
Links | BibTeX | Tags: across, admire, eumaster4hpc, eupex, eupilot, textarossa
@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}
}
Bruno Casella
Benchmarking FedAvg and FedCurv for Image Classification Tasks Miscellaneous
ITADATA, 2022.
Abstract | Links | BibTeX | Tags: eupilot
@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}
}
Presentation of the paper "Benchmarking FedAvg and FedCurv for Image Classification Tasks" to the first italian conference on Big Data and Data Science
Marco Aldinucci
Il calcolo parallelo: una storia di metodi e algoritmi raccontata dalle macchine Miscellaneous
Olimpiadi di Informatica, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags: across, admire, eumaster4hpc, eupex, eupilot, textarossa
@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}
}
Lectio Magistralis alle finali nazionali delle Olimpiadi di Informatica 2022
Marco Aldinucci
La convergenza HPC-cloud è l'anello mancante tra il calcolo scientifico e l'IA applicata Miscellaneous
Intelligenza Artificiale e Business Applications, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags: across, admire, eumaster4hpc, eupex, eupilot, textarossa
@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}
}
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.
Iacopo Colonnelli, Marco Aldinucci
Hybrid Workflows For Large-Scale Scientific Applications Miscellaneous
6th EAGE High Performance Computing Workshop, 2022.
Abstract | Links | BibTeX | Tags: across, eupex, jupyter-workflow, textarossa
@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}
}
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.
Iacopo Colonnelli, Marco Aldinucci
CINI HPC-KTT: HPC Key Technologies and Tools National Lab Miscellaneous
NVIDIA HPC Roundtable, 2022, (Invited talk).
Links | BibTeX | Tags: across, admire, eumaster4hpc, eupex, eupilot, textarossa
@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}
}
Iacopo Colonnelli, Dario Tranchitella
Dossier: multi-tenant distributed Jupyter Notebooks Miscellaneous
DoK Talks 141, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags: across, deephealth, hpc4ai, jupyter-workflow
@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}
}
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.
Iacopo Colonnelli
StreamFlow Miscellaneous
2nd HealthyCloud Workshop: Analysis of existing orchestration mechanisms for distributed computational analyses, 2022, (Invited talk).
Links | BibTeX | Tags: across, deephealth, eupex, streamflow, textarossa
@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}
}
Marco Aldinucci
HPC-cloud convergence is the missing link between scientific computing and applied-AI Miscellaneous
Machine Learning for Astrophysics (ML4ASTRO), 2022, (Keynote talk).
Abstract | Links | BibTeX | Tags: across, deephealth, eupex, eupilot
@misc{22:ml4astrotalk,
title = {HPC-cloud convergence is the missing link between scientific computing and applied-AI},
author = {Marco Aldinucci},
url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},
year = {2022},
date = {2022-06-01},
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}
}
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.
Marco Aldinucci
EuroHPC and the Italian HPC ecosystem Miscellaneous
Critical Infrastructure Protection Forum - EuroCC Romania, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags: across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa
@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}
}
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.
Marco Aldinucci
The Italian HPC ecosystem and the next generation of EuroHPC CoE Miscellaneous
EuroHPC EoCoE final summit, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags: across, admire, eumaster4hpc, eupex, eupilot, icsc, textarossa
@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}
}
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.
Iacopo Colonnelli, Marco Aldinucci
T4.1: Streaming models Miscellaneous
TEXTAROSSA General Meeting, 2022.
Links | BibTeX | Tags: textarossa
@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}
}
Iacopo Colonnelli
StreamFlow: a topology-aware WMS Miscellaneous
ELIXIR Cloud, Data & AAI Bi-weekly Technical Calls, 2022, (Invited talk).
Links | BibTeX | Tags: across, deephealth, eupex, streamflow, textarossa
@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}
}
Marco Aldinucci
From small files to no files Miscellaneous
6th Workshop on Performance and Scalability of Storage Systems, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags: admire, eupex
@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}
}
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.
Gianluca Mittone
Python for the human heart: a case study Miscellaneous
2022 Python Conference, 2022, (Invited talk).
Abstract | Links | BibTeX | Tags:
@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}
}
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.
Marco Aldinucci
Cognitive continuum: a game theoretical approach Miscellaneous
HiPEAC Vision meeting, Brussels, 16 May 2022, 2022.
Abstract | Links | BibTeX | Tags: across, admire, brainteaser, eumaster4hpc, eupex, eupilot, textarossa
@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}
}
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
Marco Aldinucci
Da HPC4AI al living lab dello spoke FutureHPC del Centro Nazionale HPC Miscellaneous
Condivisioni, Conferenza GARR 2022, 2022, (Keynote talk).
Abstract | Links | BibTeX | Tags: across, admire, eumaster4hpc, eupex, hpc4ai, icsc, textarossa
@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}
}
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.
Iacopo Colonnelli
StreamFlow: A framework for hybrid workflows Miscellaneous
EUPEX WP5 bi-weekly meeting, 2022.
Links | BibTeX | Tags: eupex, streamflow
@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}
}
Iacopo Colonnelli, Dario Tranchitella
OpenDeepHealth: Crafting a Deep Learning Platform as a Service with Kubernetes Miscellaneous
J on The Beach 2022, 2022.
Links | BibTeX | Tags: across, deephealth, hpc4ai, jupyter-workflow, streamflow
@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}
}
Iacopo Colonnelli
Distributed workflows with Jupyter Miscellaneous
J on The Beach 2022, 2022, (Workshop).
Links | BibTeX | Tags: across, deephealth, jupyter-workflow, streamflow
@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}
}
Marco Aldinucci
Parallel Computing: a simple introduction, indeed an abstraction Miscellaneous
I Lincei per la Scuola: scienze per l'innovazione digitale, 2022, (Invited talk).
@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}
}
Bruno Casella
Transfer Learning via Test-Time Neural Networks Aggregation Miscellaneous
VISAPP, 2022.
Abstract | Links | BibTeX | Tags:
@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}
}
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
Iacopo Colonnelli
StreamFlow: A framework for hybrid workflows Miscellaneous
ACROSS WP4 meeting, 2022.
Links | BibTeX | Tags: across, streamflow
@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}
}
Bruno Casella
Poster Session VISIGRAPP 2022 Miscellaneous
VISIGRAPP 2022, 2022, (Session chair).
BibTeX | Tags:
@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}
}
Iacopo Colonnelli
The OpenDeepHealth toolkit Miscellaneous
DeepHealth Winter School, 2022.
Links | BibTeX | Tags: deephealth, hpc4ai
@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}
}
2021
Marco Aldinucci, Sergio Rabellino
[No title] Miscellaneous
Vertiv keep it running tour, 2021, (Invited talk).
@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}
}
Iacopo Colonnelli
StreamFlow: A framework for hybrid workflows Miscellaneous
ACROSS WP4 meeting, 2021.
Links | BibTeX | Tags: across, streamflow
@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}
}
Marco Aldinucci
The modernization of HPC applications for the cloud era Miscellaneous
Fifth EAGE Workshop on High Performance Computing for Upstream, 2021, (Keynote talk).
Abstract | BibTeX | Tags: across, admire, deephealth, streamflow
@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}
}
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.
Iacopo Colonnelli
HPC Containers Miscellaneous
ACROSS WP4 meeting, 2021.
@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}
}
Marco Aldinucci
From skeletons to workflows in the cloud-edge era Miscellaneous
14th Intl. Symposium on High-Level Programming and Applications (HLPP), 2021, (Keynote talk).
Abstract | Links | BibTeX | Tags: across, admire, deephealth, streamflow
@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}
}
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.
Marco Aldinucci
Reproducibility in the AI era Miscellaneous
Penta Scientific Meeting, 2021.
Abstract | Links | BibTeX | Tags: across, admire, deephealth
@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}
}
TBD
Marco Aldinucci
DeepHealth perspective Miscellaneous
Future challenges in IoT, AI, and convergence of HPC & Cloud & Big Data – BDVA Data Week, 2021.
Abstract | BibTeX | Tags: deephealth
@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}
}
TBD
Marco Aldinucci
The Italian research on HPC key technologies across EuroHPC Miscellaneous
2021.
Abstract | Links | BibTeX | Tags: across, admire, eupex, eupilot, textarossa
@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}
}
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.
Marco Aldinucci, Marco Beccuti
DeepHealth: Deep Learning ad alte prestazioni per applicazioni in ambito medico Miscellaneous
Reserach meeting of the PoloICT, 2021.
Links | BibTeX | Tags: deephealth, hpc4ai
@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}
}
Marco Aldinucci, Marco Beccuti
HPC4AI: Un sistema per la ricerca e l'innovazione dei servizi cloud per l'Intelligenza Artificiale Miscellaneous
Reserach meeting of the PoloICT, 2021.
@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}
}
Marco Aldinucci, Iacopo Colonnelli
The Universal Cloud-HPC Pipeline for the AI-Assisted Explainable Diagnosis of COVID-19 Pneumonia Miscellaneous
NVidia GTC'21, 2021, (Invited talk).
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, streamflow
@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}
}
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.
Iacopo Colonnelli
StreamFlow: cross breeding cloud with HPC Miscellaneous
2021 CWL Mini Conference, 2021, (Invited talk).
Abstract | Links | BibTeX | Tags: deephealth, streamflow
@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}
}
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.
Marco Aldinucci
On HPC, AI and their Fatal Attraction Miscellaneous
CNR IEIIT, Thursday seminars (11 Feb 2021), 2021, (Invited talk).
Links | BibTeX | Tags: deephealth, hpc4ai
@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}
}
Marco Aldinucci
HPC4AI: A cloud-HPC ecosystem designed for research and innovation Miscellaneous
Advanced Computing Workshop 2021: HPC and Beyond, 2021, (Invited talk).
Abstract | Links | BibTeX | Tags: hpc4ai
@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}
}
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).
Marco Aldinucci
Lung Nodules Segmentation in CT scans by DeepHealth toolkit Miscellaneous
25th Intl. Conference on Pattern Recognition, 2021, (Demo).
Links | BibTeX | Tags: deephealth
@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}
}
Marco Aldinucci
HPC application cloudification: the streamflow toolkit Miscellaneous
PARMA-DITAM (co-localed with HiPEAC), 2021, (Keynote talk).
Links | BibTeX | Tags: deephealth, hpc4ai
@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}
}
Marco Aldinucci
High-performance computing and AI team up for COVID-19 diagnostic imaging Miscellaneous
AIhub, 2021, ((magazine)).
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai
@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}
}
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.
2020
Iacopo Colonnelli, Sergio Rabellino
JupyterFlow: Jupyter Notebooks su larga scala Miscellaneous
Workshop GARR 2020, 2020.
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai, jupyter-workflow
@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}
}
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.
Marco Aldinucci
Polmonite da COVID-19, diagnosi con l'intelligenza artificiale: Italia in prima fila Miscellaneous
Agenda Digitale, 2020, ((magazine)).
Abstract | Links | BibTeX | Tags: deephealth, hpc4ai
@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}
}
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
Iacopo Colonnelli
StreamFlow: cross breeding cloud with HPC Miscellaneous
HPC-Europa3 2nd Transnational Access Meeting (TAM), 2020, (Invited talk).
Abstract | Links | BibTeX | Tags: streamflow
@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}
}
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.
Marco Aldinucci
The DeepHealth project Miscellaneous
HPC, Big Data, IoT and AI future industry-driven collaborative strategic topics virtual workshop — HPC / HPDA spectrum, 2020, (Invited talk).
Links | BibTeX | Tags: deephealth
@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}
}
Marco Aldinucci
Building avenues for AI-assisted diagnosis over the bridge from HPC to AI Miscellaneous
CLAIRE COVID webinar, 2020, (Invited talk).
Links | BibTeX | Tags: deephealth
@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}
}
Marco Aldinucci
StreamFlow: cross-breeding cloud with HPC Miscellaneous
Computability in Europe 2020 (CIE), 2020, (Invited talk).
Links | BibTeX | Tags: deephealth
@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}
}
Marco Aldinucci
Machine Learning: the treacherous journey from data to knowledge (with examples from HPC4AI@UNITO platform) Miscellaneous
Machine Learning Meets Chemistry @ the Department of Chemistry, University of Torino, 2020, (Invited talk).
Links | BibTeX | Tags: deephealth, hpc4ai
@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}
}
Marco Aldinucci
HPC4AI: From Enabling Platforms to Technology Sovereignty to Innovation Miscellaneous
Elixir-Italia meeting, 2020, (Invited talk).
@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}
}
Marco Aldinucci
Mnemocomputing Miscellaneous
HLPGPU 2019 (Satellite workshop of HiPEAC 2019), 2020, (Keynote talk).
@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}
}
2019
Marco Aldinucci
How Artificial Intelligence is shaping the Future of Health Miscellaneous
1st Industrial Conference on Artificial Intelligence and health, 2019, (Invited talk).
@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}
}
Iacopo Colonnelli
StreamFlow: un approccio dichiarativo a workflow e pipeline di micro-servizi Miscellaneous
Workshop GARR 2019, 2019.
Abstract | Links | BibTeX | Tags: streamflow
@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}
}
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.
Marco Aldinucci
Towards DeepLearning at Scale Miscellaneous
SPPEXA Final Symposium, 2019, (Invited talk).
BibTeX | Tags:
@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}
}
Marco Aldinucci
The Joint Undertaking EuroHPC Miscellaneous
Giornata Nazionale di Lancio dei bandi Information and Communication Technologies in Horizon 2020, 2019, (Invited talk).
BibTeX | Tags:
@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}
}
Marco Aldinucci
HPC4AI, an on-demand federated platform endeavour Miscellaneous
Ospedale San Raffaele, 2019, (Invited talk).
@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}
}
Marco Aldinucci
L'infrastruttura necessaria per creare interoperabilità tra pubbliche amministrazioni Miscellaneous
Convegno ``L'amministrazione pubblica con i Big data'', 2019, (Invited talk).
@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}
}
Marco Aldinucci
High-Performance Computing Miscellaneous
Istituto per la Competitività, 2019, (Invited talk).
@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}
}
Marco Aldinucci, Claudio Berzovini, Costantino Grana, Marco Grangetto, Luca Pireddu, Gianluigi Zanetti
Deep Learning e calcolo ad alte prestazioni per l'elaborazione di immagini biomediche Miscellaneous
Ital-IA: Convegno Nazionale CINI sull'Intelligenza Artificiale, 2019.
Abstract | BibTeX | Tags: hpc4ai
@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}
}
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.
Iacopo Colonnelli
Deep Learning at Scale Miscellaneous
27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019), 2019.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
Iacopo Colonnelli
Accelerating spectral graph analysis through wavefronts of linear algebra operations Miscellaneous
27th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP 2019), 2019.
Abstract | Links | BibTeX | Tags:
@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}
}
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.
Marco Aldinucci
The evolution of high-performance systems: from HPC to Big Data to Deep Learning Miscellaneous
4th Open SmartData@PoliTO Workshop, 2019, (Invited talk).
@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}
}
2018
Marco Aldinucci
L'evoluzione delle piattaforme e dei sistemi ad alte prestazioni: da HPC ai Big Data al Deep Learning Miscellaneous
Chimica passione periodica: Big Data: Modelli predittivi, simulazione, analisi, Dipartimento di Chimica, Università di Torino, 2018, (Invited talk).
@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}
}
Marco Aldinucci
Le piattaforme AI-on-demand come fattore di l'innovazione nelle PMI Miscellaneous
CITYLAB Ecosystem: i dati per le aziende, le città e le università, Nuvola Lavazza Torino, 2018, (Invited talk).
@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}
}
Marco Aldinucci
HPC4AI, an AI-on-demand federated platform endeavour Miscellaneous
ACM Computing Frontiers, 2018, (Invited talk).
@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}
}
Marco Aldinucci
Toward Near-Data Processing service computing Miscellaneous
Parallel, Distributed, and Network-Based Processing (PDP), 2018, (Keynote talk).
@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}
}
Marco Aldinucci
From mutexes to lock-free to atomic-less and back to (un-contended) mutex: A story in which energy efficiency is unexpectedly constantly increasing Miscellaneous
Arm Research ltd, 2018, (Invited talk).
BibTeX | Tags:
@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}
}
Marco Aldinucci
Designing a heterogeneous federated data center for research Miscellaneous
DiSIA, University of Florence, 2018, (Invited talk).
BibTeX | Tags:
@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}
}
2017
Marco Aldinucci
Partitioned Global Address Space in the mainstream of C++ programming Miscellaneous
International Parallel Computing (ParCo), 2017, (Keynote talk).
@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}
}
Marco Aldinucci
OCCAM: Heterogeneous platforms mixed blessing of code optimisation Miscellaneous
Riunione della Commissione Calcolo e Reti dell'INFN, 2017, (Invited talk).
@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}
}
Marco Aldinucci
Streaming in the PGAS Era Miscellaneous
International Workshop on Autonomic Solutions for Parallel and Distributed Data Stream Processing (Auto-DaSP 2017), Workshop of EuroPar 2017, 2017, (Keynote talk).
@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}
}
Marco Aldinucci
Designing a heterogeneous federated data center for research Miscellaneous
Euro-Par 2017, 2017.
@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}
}
2016
Marco Aldinucci
HPC come piattaforma abilitante: rischi e opportunità Miscellaneous
Workshop of the Competence Centre on Scientific Computing of University of Torino, 2016, (Invited talk).
@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}
}
Marco Aldinucci
Atomic operations considered hARMful Miscellaneous
ARM Research Summary, 2016.
@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}
}
Marco Aldinucci
I supercomputer venuti dal futuro: Exascale Computing Miscellaneous
International Pint of Science, Officine ferroviarie, 2016, (Invited talk).
BibTeX | Tags:
@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}
}
Marco Aldinucci
Composition and Compartmentalisation As Enabling Features for Data-Centric, Extreme Scale Applications: An MPI-X Approach Miscellaneous
SIAM Conference on Parallel Processing for Scientific Computing, 2016.
@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}
}
Marco Aldinucci
[No title] Miscellaneous
FastData@UNITO opening, 2016, (Invited talk).
BibTeX | Tags:
@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}
}
2014
Marco Aldinucci
Parallel patterns, data-centric concurrency, and heterogeneous computing Miscellaneous
6th IEEE Intl. Conference on High-Performance Computing and Communications (HPCC), 2014, (Keynote talk).
@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}
}
Marco Aldinucci
FastFlow: high-level programming patterns with non-blocking lock-free run-time support Miscellaneous
Intl. Summer School in Parallel Patterns, 2014, (Keynote talk).
@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}
}
Marco Aldinucci
An Overview of FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs Miscellaneous
GPU Technology Conference (GTC 2014), 2014.
@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}
}
Marco Aldinucci
FastFlow: Combining Pattern-Level Abstraction and Efficiency in GPGPUs Miscellaneous
GPU Technology Conference (GTC 2014), 2014.
@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}
}
2013
Marco Aldinucci
Turning Big data into knowledge: systems biology droplets in the cloud Miscellaneous
Cloud and Science seminar at ``the Ramon Areces Foundation'', 2013, (invited talk).
@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}
}
2012
Marco Aldinucci
A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising Miscellaneous
IEEE Intl. Conference on Image Processing Theory, Tools and Applications (IPTA), 2012.
@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}
}
Marco Aldinucci
Bio-IT World Europe, 2012, (invited talk).
@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}
}
Marco Aldinucci
FastFlow: high-level programming patterns with non-blocking lock-free run-time support Miscellaneous
UPMARC Workshop on Task-Based Parallel Programming, 2012, (invited talk).
@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}
}
Marco Aldinucci
Workshop in big data management, 2012.
@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}
}
Marco Aldinucci
Pattern-based Parallel Edge Preserving Algorithm for Salt-and-Pepper Image Denoising Miscellaneous
HPC Advisory Council Switzerland Conference, 2012, (invited talk).
@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}
}
2011
Marco Aldinucci
The camera that makes you beauty (with a novel real-time video-denoiser algorithm) Miscellaneous
TOSM expo, 2011, (invited talk).
BibTeX | Tags:
@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}
}
Marco Aldinucci
Performance and productivity in the multi-core era: challenges in software engineering and formal methods Miscellaneous
IMT, 2011, (invited talk).
@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}
}
Marco Aldinucci
FastFlow: Performance and Productivity in the Exascale Era Miscellaneous
IBM Research, Exascale laboratory, 2011, (invited talk).
BibTeX | Tags:
@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}
}
Marco Aldinucci
FastFlow: Performance and Productivity in the Multicore Era Miscellaneous
Formal Methods for Components and Objects (FMCO), 2011, (invited talk).
@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}
}
Marco Aldinucci
MyMed: Un social network geosensibile per reti fisse e mobili basato sul paradigma Peer-to-Peer Miscellaneous
La notte dei ricercatori, 2011, (invited talk).
@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}
}
Marco Aldinucci
On Parallelizing On-Line Statistics for Stochastic Biological Simulations Miscellaneous
Workshop on High Performance Bioinformatics and Biomedicine (HiBB), 2011.
@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}
}
Marco Aldinucci
Accelerating code on multi-cores with FastFlow Miscellaneous
Euro-Par 2011, 2011.
@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}
}
Marco Aldinucci
High-level parallel programming: (few) ideas for challenges in formal methods Miscellaneous
COST Action IC701 workshop, 2011, (invited talk).
@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}
}
Marco Aldinucci
On Designing Multicore-Aware Simulators for Biological Systems Miscellaneous
IEEE Euromicro PDP 2011: Parallel Distributed and network-based Processing, 2011.
@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}
}
2010
Marco Aldinucci
FastFlow: a pattern-based programming framework for multicores Miscellaneous
seminar 10191, 2010, (invited talk).
@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}
}
Marco Aldinucci
Efficient Smith-Waterman on multi-core with FastFlow Miscellaneous
PDP 2010: Parallel Distributed and network-based Processing, 2010.
@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}
}
2009
Marco Aldinucci
Efficient streaming applications on multi-core with FastFlow: the biosequence alignment test-bed Miscellaneous
ParCo 2009, 2009.
@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}
}
Marco Aldinucci
STKM on SCA: A unified framework with components, workflows and algorithmic skeletons Miscellaneous
EuroPar 2009, 2009.
@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}
}
Marco Aldinucci
Towards Hierarchical Management of Autonomic Components: a Case Study Miscellaneous
Euromicro PDP 2009: Parallel Distributed and network-based Processing, 2009.
@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}
}
Marco Aldinucci, Sonia Campa, Patrizio Dazzi, Nicola Tonellotto, Giorgio Zoppi
D.NFCF.05 – NFCF tuned prototype and final documentation Miscellaneous
url http://gridcomp.ercim.org/, 2009.
BibTeX | Tags:
@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}
}
2008
Marco Aldinucci
Towards a Formal Semantics for Autonomic Components Miscellaneous
CoreGRID Symposium, 2008.
BibTeX | Tags:
@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}
}
Marco Aldinucci, Sonia Campa, Patrizio Dazzi, Nicola Tonellotto, Giorgio Zoppi
D.NFCF.03 – Methodology to derive performance models for component and composite components Miscellaneous
url http://gridcomp.ercim.org/, 2008.
BibTeX | Tags:
@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}
}
Marco Aldinucci, Sonia Campa, Patrizio Dazzi, Nicola Tonellotto, Giorgio Zoppi
D.NFCF.04 – NFCF prototype and early documentation Miscellaneous
url http://gridcomp.ercim.org/, 2008.
BibTeX | Tags:
@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}
}
Marco Aldinucci
CoreGRID Institute on Programming Model Miscellaneous
Open Grid Forum (OGF), CoreGRID Industrial Showcase, 2008, (Poster).
@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}
}
Marco Aldinucci
Autonomic components in GCM Miscellaneous
CoreGRID Scientific Advisory Board, 2008, (invited talk).
@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}
}
2007
Marco Aldinucci
VirtuaLinux: Una soluzione open source per il clustering HPC Miscellaneous
Net & System Security, 2007, (invited talk in italian).
@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}
}
Marco Aldinucci, Sonia Campa, Patrizio Dazzi, Nicola Tonellotto
D.NFCF.01 – Non functional component subsystem architectural design Miscellaneous
url http://gridcomp.ercim.org/, 2007.
BibTeX | Tags:
@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}
}
Marco Aldinucci, Sonia Campa, Patrizio Dazzi, Nicola Tonellotto
D.NFCF.02 – Non functional component subsystem architectural design (code) Miscellaneous
url http://gridcomp.ercim.org/, 2007.
BibTeX | Tags:
@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}
}
Marco Aldinucci
Behavioural skeletons for component autonomic management on grids Miscellaneous
CoreGRID Workshop on Grid Programming Model, Grid and P2P Systems Architecture, Grid Systems, Tools and Environments, 2007.
@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}
}
2006
Marco Aldinucci
Taming the grid through dynamic adaptation: results and open problems Miscellaneous
Last Advances in Computer Science, 2006, (invited talk).
@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}
}
Marco Aldinucci
Fault-tolerant data sharing for high-level grid programming: a hierarchical storage architecture Miscellaneous
CoreGRID Integration Workshop, 2006.
@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}
}
Marco Aldinucci
Autonomic QoS in ASSIST Grid-aware components Miscellaneous
IEEE Euromicro PDP 2006: Parallel Distributed and network-based Processing, 2006.
@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}
}
2005
Marco Aldinucci
Building Interoperable Grid-aware ASSIST Applications via Web Services Miscellaneous
ParCo 2005, 2005.
@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}
}
Marco Aldinucci
Towards a distributed scalable data service for the Grid Miscellaneous
ParCo 2005, 2005.
@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}
}
Marco Aldinucci
Dynamic reconfiguration of grid-aware applications in ASSIST Miscellaneous
Euro-Par 2005, 2005.
@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}
}
2004
Marco Aldinucci
Rendering Grid Heterogeneity Harmless Miscellaneous
Seminar 04451 – Future Generation Grids, 2004, (invited talk).
@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}
}
Marco Aldinucci
Accelerating Apache farms through ad-HOC distributed scalable object repository Miscellaneous
Euro-Par 2004, 2004.
@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}
}
Marco Aldinucci
Optimization techniques for implementing parallel skeletons in Grid Miscellaneous
CMPP 2004 (in conjunction with MPC 04), 2004.
@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}
}
2003
Marco Aldinucci
An operational semantics for skeletons Miscellaneous
ParCo 2003, 2003.
@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}
}
Marco Aldinucci
A framework for experimenting with structured parallel programming environment design Miscellaneous
ParCo 2003, 2003.
@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}
}
Marco Aldinucci
ASSIST demo: a high level, high performance, portable, structured parallel programming environment at work Miscellaneous
9th Intl Euro-Par 2003: Parallel and Distributed Computing, 2003.
@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}
}
Marco Aldinucci
eskimo: experimenting skeletons on the shared address model Miscellaneous
HLPP 2003, 2003.
@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}
}
2000
Marco Aldinucci
The META transformation tool for skeleton-based language Miscellaneous
CMPP 2000, 2000.
@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}
}