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Financement de l’UE (3 999 650 €) : Algorithmes SCalables économes en énergie pour la prévision météorologique et climatique à l’échelle exascale Hor07/06/2018 Programme de recherche et d'innovation de l'UE « Horizon »
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Algorithmes SCalables économes en énergie pour la prévision météorologique et climatique à l’échelle exascale
ESCAPE-2 will develop world-class, extreme-scale computing capabilities for European operational numerical weather and climate prediction, and provide the key components for weather and climate domain benchmarks to be deployed on extreme-scale demonstrators and beyond. This will be achieved by developing bespoke and novel mathematical and algorithmic concepts, combining them with proven methods, and thereby reassessing the mathematical foundations forming the basis of Earth system models. ESCAPE-2 also invests in significantly more productive programming models for the weather-climate community through which novel algorithm development will be accelerated and future-proofed. Eventually, the project aims at providing exascale-ready production benchmarks to be operated on extreme-scale demonstrators (EsD) and beyond. ESCAPE-2 combines cross-disciplinary uncertainty quantification tools (URANIE) for high-performance computing, originating from the energy sector, with ensemble based weather and climate models to quantify the effect of model and data related uncertainties on forecasting – a capability, which weather and climate prediction has pioneered since the 1960s. The mathematics and algorithmic research in ESCAPE-2 will focus on implementing data structures and tools supporting parallel computation of dynamics and physics on multiple scales and multiple levels. Highly-scalable spatial discretization will be combined with proven large time-stepping techniques to optimize both time-to-solution and energy-to-solution. Connecting multi-grid tools, iterative solvers, and overlapping computations with flexible-order spatial discretization will strengthen algorithm resilience against soft or hard failure. In addition, machine learning techniques will be applied for accelerating complex sub-components. The sum of these efforts will aim at achieving at the same time: performance, resilience, accuracy and portability.
| Barcelona Supercomputing Center Centro Nacional de Supercomputacion | 232 500 € |
| Bull SAS | 242 994 € |
| Commissariat a L Energie Atomique et aux Energies Alternatives | 356 498 € |
| Danmarks Meteorologiske Institut | 307 000 € |
| Deutsches Klimarechenzentrum GmbH | 255 000 € |
| Eidgenoessisches Departement DES Innern | 439 750 € |
| European Centre for Medium-Range Weather Forecasts | 767 549 € |
| Fondazione Centro Euro-Mediterraneosui Cambiamenti Climatici | 130 000 € |
| Institut Royal Meteorologique de Belgique | 387 485 € |
| Loughborough University | 359 000 € |
| MAX-Planck-Gesellschaft ZUR Forderung DER Wissenschaften e. V. | 265 625 € |
| Politecnico Di Milano | 256 250 € |
https://cordis.europa.eu/project/id/800897
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