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Section: Partnerships and Cooperations

International Initiatives

We are involved in the Inria@SiliconValley initiative through the associate team FASTLA described below.

Inria Associate Teams not involved in an Inria International Labs

MORSE
  • Title: Matrices Over Runtime Systems @ Exascale

  • International Partner (Institution - Laboratory - Researcher):

    • KAUST Supercomputing Laboratory (USA)

  • Duration: 2014 - 2016

  • See also: http://icl.cs.utk.edu/projectsdev/morse/index.html

  • The goal of Matrices Over Runtime Systems at Exascale (MORSE) project is to design dense and sparse linear algebra methods that achieve the fastest possible time to an accurate solution on large-scale multicore systems with GPU accelerators, using all the processing power that future high end systems can make available. To develop software that will perform well on petascale and exascale systems with thousands of nodes and millions of cores, several daunting challenges have to be overcome, both by the numerical linear algebra and the runtime system communities. By designing a research framework for describing linear algebra algorithms at a high level of abstraction,the MORSE team will enable the strong collaboration between research groups in linear algebra, runtime systems and scheduling needed to develop methods and libraries that fully benefit from the potential of future large-scale machines. Our project will take a pioneering step in the effort to bridge the immense software gap that has opened up in front of the High-Performance Computing (HPC) community.

FASTLA
  • Title: Fast and Scalable Hierarchical Algorithms for Computational Linear Algebra

  • International Partner (Institution - Laboratory - Researcher):

    • Stanford University (USA)

    • Lawrence Berkeley National Laboratory (USA)

  • Duration: 2014 - 2016

  • See also: http://people.bordeaux.inria.fr/coulaud/projets/FastLA_Website/

  • In this project, we propose to study fast and scalable hierarchical numerical kernels and their implementations on heterogeneous manycore platforms for two major computational kernels in intensive challenging applications. Namely, fast multipole methods (FMM) and sparse hybrid linear solvers, that appear in many intensive numerical simulations in computational sciences. Regarding the FMM we plan to study novel generic formulations based on -matrices techniques, that will be eventually validated in the field of material physics: the dislocation dynamics. For the hybrid solvers, new parallel preconditioning approaches will be designed and the use of -matrices techniques will be first investigated in the framework of fast and monitored approximations on central components. Finally, the innovative algorithmic design will be essentially focused on heterogeneous manycore platforms. The partners, Inria HiePACS, Lawrence Berkeley Nat. Lab and Stanford University, have strong, complementary and recognized experiences and backgrounds in these fields.

Participation In other International Programs

HOSCAR

We are involved in the Inria-CNPq HOSCAR project led by Stéphane Lanteri.

The general objective of the project is to setup a multidisciplinary Brazil-France collaborative effort for taking full benefits of future high-performance massively parallel architectures. The targets are the very large-scale datasets and numerical simulations relevant to a selected set of applications in natural sciences: (i) resource prospection, (ii) reservoir simulation, (iii) ecological modeling, (iv) astronomy data management, and (v) simulation data management. The project involves computer scientists and numerical mathematicians divided in 3 fundamental research groups: (i) numerical schemes for PDE models (Group 1), (ii) scientific data management (Group 2), and (iii) high-performance software systems (Group 3).

The final annual meeting has been organized in Inria Sophia, on September 21-24, 2015, while a follow-up of the project will exist as a H2020 project entitles HPC4E (HPC for Energy) to be started in 2016 with an enlarged partnership.