Talks | Parallel Computing
2023
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
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.
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, 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
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}
}
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.
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
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.
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
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}
}
2020
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, 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
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}
}
2019
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, 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.