Description du projet
Un flux de travail intelligent pour les écosystèmes de calcul haute performance
Avec la prolifération de l’utilisation des ordinateurs, de l’électronique et de l’internet des objets, le développement et l’innovation dans le domaine de l’informatique et du calcul haute performance (CHP) sont fortement sollicités. Pour surmonter les défis actuels auxquels sont confrontés les écosystèmes de CHP et ouvrir la voie à de nouvelles innovations, le projet eFlows4HPC, financé par l’UE, répondra au manque d’outils nécessaires au développement de flux de travail complexes impliquant le CHP en fournissant une méthodologie qui élargira et facilitera l’utilisation des flux de travail par les communautés de CHP actuelles et nouvelles: Les flux de travail CHP en tant que service (HPCWaaS). Ce service comprendra une pile logicielle de flux de travail ainsi que plusieurs services permettant la mise en œuvre efficace de simulations de CHP, de modélisation, d’apprentissage automatique et d’analyse de mégadonnées dans les secteurs scientifiques et industriels.
Objectif
Today, developers lack tools that enable the development of complex workflows involving HPC simulation and modelling with data analytics (DA) and machine learning (ML). TheFlows4HPC aims to deliver a workflow software stack and an additional set of services to enable the integration of HPC simulation and modelling with big data analytics and machine learning in scientific and industrial applications. The software stack will allow to develop innovative adaptive workflows that efficiently use the computing resources and also considering innovative storage solutions.
To widen the access to HPC to newcomers, the project will provide HPC Workflows as a Service (HPCWaaS), an environment for sharing, reusing, deploying and executing existing workflows on HPC systems. The workflow technologies, associated machine learning and big data libraries used in the project leverages previous open source European initiatives. Specific optimization tasks for the use of accelerators (FPGAs, GPUs) and the EPI will be performed in the project use cases.
To demonstrate the workflow software stack, use cases from three thematic pillars have been selected. Pillar I focuses on the construction of DigitalTwins for the prototyping of complex manufactured objects integrating state-of-the-art adaptive solvers with machine learning and data-mining, contributing to the Industry 4.0 vision. Pillar II develops innovative adaptive workflows for climate and for the study of Tropical Cyclones (TC) in the context of the CMIP6 experiment, including in-situ analytics. Pillar III explores the modelling of natural catastrophes - in particular, earthquakes and their associated tsunamis- shortly after such an event is recorded. Leveraging two existing workflows, the Pillar will work of integrating them with the eFlows4HPC software stack and on producing policies for urgent access to supercomputers. The pillar results will be demonstrated in the target community CoEs to foster adoption and get feedback.
Champ scientifique
- natural sciencescomputer and information sciencessoftware
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesearth and related environmental sciencesgeologyseismology
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwaresupercomputers
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Mots‑clés
Programme(s)
Régime de financement
IA - Innovation actionCoordinateur
08034 Barcelona
Espagne