Talks | Parallel Computing
2024
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}
}
2023
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, eupex, eupilot
@misc{23:eurohpc,
title = {Cross-Facility Federated Learning},
author = {Iacopo Colonnelli and Robert Birke and Giulio Malenza and Gianluca Mittone and Alberto Mulone and Marco Aldinucci and Valerio Basile and Marco Antonio Stranisci and Viviana Patti and Jeroen Galjaard and Lydia Y. Chen and Sanzio Bassini and Massimiliano Guarrasi and Gabriella Scipione and Jan Martinovič and Vit Vondrák},
url = {https://datacloud.di.unito.it/index.php/s/DDAz4QkJP3WZ68M},
year = {2023},
date = {2023-12-01},
address = {Bruxelles, Belgium},
howpublished = {1st EuroHPC User Day},
keywords = {across, eupex, eupilot},
pubstate = {published},
tppubtype = {misc}
}
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}
}
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}
}
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:vcp,
title = {Porting the Variant Calling Pipeline for NGS data in cloud-HPC environment},
author = {Alberto Mulone and Sherine Awad and Davide Chiarugi and Marco Aldinucci},
editor = {Hossain Shahriar and Yuuichi Teranishi and Alfredo Cuzzocrea and Moushumi Sharmin and Dave Towey and A. K. M. Jahangir Alam Majumder and Hiroki Kashiwazaki and Ji-Jiang Yang and Michiharu Takemoto and Nazmus Sakib and Ryohei Banno and Sheikh Iqbal Ahamed},
url = {https://datacloud.di.unito.it/index.php/s/zNLj3LCZNsNxHwy},
doi = {10.1109/COMPSAC57700.2023.00288},
year = {2023},
date = {2023-06-01},
booktitle = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},
pages = {1858–1863},
publisher = {IEEE},
address = {Torino, Italy},
abstract = {In recent years we have understood the importance of analyzing and sequencing human genetic variation. A relevant aspect that emerged from the Covid-19 pandemic was the need to obtain results very quickly; this involved using High-Performance Computing (HPC) environments to execute the Next Generation Sequencing (NGS) pipeline. However, HPC is not always the most suitable environment for the entire execution of a pipeline, especially when it involves many heterogeneous tools. The ability to execute parts of the pipeline on different environments can lead to higher performance but also cheaper executions. This work shows the design and optimization process that led us to a state-of-the-art Variant Calling hybrid workflow based on the StreamFlow Workflow Management System (WfMS). We also compare StreamFlow with Snakemake, an established WfMS targeting HPC facilities, observing comparable performance on single environments and satisfactory improvements with a hybrid cloud-HPC configuration.},
howpublished = {47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023},
keywords = {across, icsc, streamflow},
pubstate = {published},
tppubtype = {misc}
}
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.
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.
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, 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
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
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
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:ml4astro,
title = {HPC-cloud convergence is the missing link between scientific computing and applied-AI},
author = {Marco Aldinucci},
editor = {Filomena Bufano and Simone Riggi and Eva Sciacca and Francesco Schilliro},
url = {https://datacloud.di.unito.it/index.php/s/2SGswkcip7MoMoH},
doi = {10.1007/978-3-031-34167-0_39},
year = {2022},
date = {2022-06-01},
booktitle = {Astrophysics and Space Science Proceedings},
volume = {60},
pages = {193–199},
publisher = {Springer},
address = {Catania, Italy},
abstract = {First, HPC infrastructures are embracing GPUs for their superior performance-per-watt ratio against general-purpose multicores. Second, the next-generation scientific workflows are integrating AI-based steps for their accuracy in approximating and analyzing complex phenomena. Third, AI and specifically Machine Learning (ML), is a perfect workload for GPUs in terms of performance and development time. Today, we cannot still close the circle seamlessly running AI-enabled scientific workloads into HPC infrastructures because their system software and development tools are not designed for modern workloads, such as ML frameworks designed for the cloud. HPC-cloud convergence is likely to bridge the gap. In the talk, we will present Streamflow and CAPIO, two development tools for HPC-cloud convergence.},
howpublished = {Machine Learning for Astrophysics (ML4ASTRO)},
note = {Keynote talk},
keywords = {across, deephealth, eupex, eupilot},
pubstate = {published},
tppubtype = {misc}
}
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
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, 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}
}
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}
}
2021
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
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.