Description du projet
Le CHP au service des applications dans les domaines de l’aéronautique, du climat et de la météo, et de l’énergie
Pour garder une longueur d’avance dans la résolution des défis industriels, technologiques et scientifiques et suivre le rythme rapide auquel les données sont générées par les expériences scientifiques et les simulations de grande envergure (c’est-à-dire les prédictions multiphysiques, climatiques et météorologiques), l’UE investit dans les superordinateurs et les technologies. Le projet ACROSS, financé par l’UE, co-concevra et développera une plateforme convergente de calcul à haute performance (CHP), de mégadonnées et d’intelligence artificielle (IA), prenant en charge des applications dans les domaines de l’aéronautique, du climat et de la météo, et de l’énergie. ACROSS combinera les techniques traditionnelles de CHP avec les techniques d’analyse de l’IA (ML/DL) et des mégadonnées afin d’améliorer les résultats des tests d’application. Le projet ACROSS encouragera la coopération avec d’autres initiatives de l’UE et les futurs projets EuroHPC afin de favoriser l’adoption des calculs exascale parles parties prenantes du domaine de test.
Objectif
Supercomputers have been extensively used to solve complex scientific and engineering problems, boosting the capability to design more efficient systems. The pace at which data are generated by scientific experiments and large simulations (e.g. multiphysics, climate, weather forecast, etc.) poses new challenges in terms of capability of efficiently and effectively analysing massive data sets. Artificial Intelligence, and more specifically Machine Learning (ML) and Deep Learning (DL) recently gained momentum for boosting simulations’ speed. ML/DL techniques are part of simulation processes, used to early detect patterns of interests from less accurate simulation results. To address these challenges, the ACROSS project will co-design and develop an HPC, BD, and Artificial Intelligence (AI) convergent platform, supporting applications in the Aeronautics, Climate and Weather, and Energy domains. To this end, ACROSS will leverage on next generation of pre-exascale infrastructures, still being ready for exascale systems, and on effective mechanisms to easily describe and manage complex workflows in these three domains. Energy efficiency will be achieved by massive use of specialized hardware accelerators, monitoring running systems and applying smart mechanisms of scheduling jobs. ACROSS will combine traditional HPC techniques with AI (specifically ML/DL) and BD analytic techniques to enhance the application test case outcomes (e.g. improve the existing operational system for global numerical weather prediction, climate simulations, develop an environment for user-defined in-situ data processing, improve and innovate the existing turbine aero design system, speed up the design process, etc.). The performance of ML/DL will be accelerated by using dedicated hardware devices. ACROSS will promote cooperation with other EU initiatives (e.g. BDVA, EPI) and future EuroHPC projects to foster the adoption of exascale-level computing among test case domain stakeholders.
Champ scientifique
- natural sciencesearth and related environmental sciencesatmospheric sciencesmeteorology
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwaresupercomputers
- natural sciencescomputer and information sciencesdata sciencedata processing
Mots‑clés
Programme(s)
Régime de financement
IA - Innovation actionCoordinateur
10138 Torino
Italie