Description du projet
L’Europe se place à l’avant-garde de l’informatique exascale grâce à un nouveau centre d’excellence
Le matériel et les logiciels informatiques avancés ont permis d’accéder à de grands ensembles de données très complexes, du génome humain au spectre des accélérateurs de particules, d’en extraire des informations et d’effectuer des prédictions. L’analyse et le traitement de mégadonnées seront cruciaux pour notre capacité à relever les défis liés au changement climatique, à l’approvisionnement énergétique à l’échelle mondiale, aux futures pandémies et à la conception de métamatériaux. Le centre d’excellence RAISE (Centre of Excellence for Research on AI- and Simulation-Based Engineering at Exascale), financé par l’UE, ciblant un milliard d’opérations par seconde, permettra de relever ces défis. L’équipe travaillera à l’établissement d’un plan pour les services et les communautés d’utilisateurs afin de garantir que l’Europe joue un rôle de premier plan dans la résolution des problèmes les plus complexes du monde.
Objectif
Compute- and data-driven research encompasses a broad spectrum of disciplines and is the key to Europe’s global success in various scientific and economic fields. The massive amount of data produced by such technologies demands novel methods to post-process, analyze, and to reveal valuable mechanisms. The development of artificial intelligence (AI) methods is rapidly proceeding and they are progressively applied to many stages of workflows to solve complex problems. Analyzing and processing big data require high computational power and scalable AI solutions. Therefore, it becomes mandatory to develop entirely new workflows from current applications that efficiently run on future high-performance computing architectures at Exascale. The Center of Excellence for Research on AI- and Simulation-based Engineering at Exascale (RAISE) will be the excellent enabler for the advancement of such technologies in Europe on industrial and academic levels, and a driver for novel intertwined AI and HPC methods. These technologies will be advanced along representative use-cases, covering a wide spectrum of academic and industrial applications, e.g. coming from wind energy harvesting, wetting hydrodynamics, manufacturing, physics, turbomachinery, and aerospace. It aims at closing the gap in full loops using forward simulation models and AI-based inverse inference models, in conjunction with statistical methods to learn from current and historical data. In this context, novel hardware technologies, i.e. Modular Supercomputing Architectures, Quantum Annealing, and prototypes from the DEEP project series will be used for exploring unseen performance in data processing. Best practices, support, and education for industry, SMEs, academia, and HPC centers on Tier-2 level and below will be developed and provided in RAISE's European network attracting new user communities. This goes along with the development of a business providing new services to various user communities.
Champ scientifique
- natural sciencescomputer and information sciencesartificial intelligence
- natural sciencescomputer and information sciencesdata sciencebig data
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energywind power
- natural sciencescomputer and information sciencesdata sciencedata processing
Mots‑clés
Programme(s)
Régime de financement
RIA - Research and Innovation actionCoordinateur
52428 Julich
Allemagne