Talks | Parallel Computing
2024
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.
2023
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.
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}
}
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}
}
2022
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, 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}
}
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
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
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
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.
2021
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.