The IPL HPC-BigData is a four year project (2018-2022) funded by INRIA.


HPC and Big Data evolved with their own infrastructures (supercomputers versus clouds), applications (scientific simulations versus data analytics) and software tools (MPI and OpenMP versus Map/Reduce or Deep Learning frameworks). But Big Data analytics is becoming more compute-intensive (thanks to deep learning), while data handling is becoming a major concern for scientific computing. The goal of this HPC-BigData IPL is to gather teams from the HPC, Big Data and Machine Learning (ML) areas to work at the intersection between these domains. Research is organized along three main axes: high performance analytics for scientific computing applications, high performance analytics for big data applications, infrastructure and resource management.

Full Initial Proposal  (Feb 2018)


INRIA Teams:

  • DataMove, Grenoble: Bruno Raffin (PI), Olivier Richard
  • KerData, Rennes: Gabriel Antoniu, Alexandru Costan
  • SequeL, Lille: Philippe Preux
  • Sierra, Paris: Francis Bach, Loic Esteve
  • Tau, Saclay: Guillaume Charpiat
  • Zenith, Montpellier: Patrick Valduriez, Alexis Joly
  • Parietal, Saclay: Gael Varoquaux
  • TADaaM, Bordeaux: Emmanuel Jeannot, Guillaume Aupy
  • HiePACS, Bordeaux: Olivier Coulaud
  • Storm, Bordeaux: Samuel Thibault
  • RealOpt, Bordeaux: Olivier Beaumont

External Participants:

  • Laboratoire de Biochimie Théorique (LBT), CNRS, Paris: Marc Baaden, Jérôme Hénin
  • Argonne National Labs,  Mathematics & Computer Science Division
  • Grid’5000: Pierre Neyron
  • Esi-Group
  • Atos/Bull


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