Section: Partnerships and Cooperations

European Initiatives

FP7 & H2020 Projects

• H2020 EoCoE-II (2019-2021)

  • Energy oriented Center of Excellence on HPC.

  • H2020 RIA european project, call H2020-INFRAEDI-2018-1.

  • PI: CEA.

  • Partners: CEA, FZL, ENEA, BSC, CNRS, Inria, CERFACS, Max-Planck-Gesellschaft, FRAUNHOFER, FAU, CNR, UNITN, PSNC, ULB, UBAH, CIEMAT, IFPEN, DDN. Datamove is leading the WP5 (Ensemble Runs)

  • Summary: The EoCoE-II project will build on its unique, established role at the crossroads of HPC and renewable energy to accelerate the adoption of production, storage and distribution of clean electricity. How will we achieve this? In its proof-of-principle phase, the EoCoE consortium developed a comprehensive, structured support pathway for enhancing the HPC capability of energy-oriented numerical models, from simple entry-level parallelism to fully-fledged exascale readiness. At the top end of this scale, promising applications from each energy domain have been selected to form the basis of 5 new Energy Science Challenges:

    • Wind turbine modelling, from detailed understanding single turbine dynamics to flow across entire wind farms in complex terrain;

    • Energy Meteorology, where probabilistic forecasting is needed to predict the production efficiency of solar and wind parks and their impact on energy trading across the grid;

    • Design and study of new energy materials for photovoltaic cells, batteries and super-capacitors;

    • Water for energy to manage geothermal and hydro-power including the influence of climate change on these resources;

    • And fusion for energy, where the mandatory kinetic modelling of plasma turbulence and transport from the core to the edge of complex tokamak magnetic geometries requires exascale resources.

Collaborations in European Programs, Except FP7 & H2020

• Program: SKŁODOWSKA-CURIE ACTIONS - Individual Fellowship

• Project acronym: DAMA

• Project title: Extreme-Scale Data Management

• Duration: November 2018 - October 2020

• Coordinator: Bruno Raffin

• Followship Recipient: Francieli Zanon Boito.

• Abstract: This project is concerned with the I/O challenges that arise from the convergence between these two different paradigms. It is clear data analytics tools cannot simply replace their typical storage solutions for the HPC I/O stack, centered on the abstraction of files and powered by a parallel file system, because their workload is not well suited for that and would observe poor performance. Moreover, the separated storage infrastructure breaks the data affinity idea in which they are built upon. Finally, even among traditional HPC applications there is a need to minimize data movement, as it imposes high latency and increases energy consumption.