Section: Partnerships and Cooperations

International Initiatives

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 (United States) - KSL - Hatem Ltaief

• Start year: 2011

• See also: http://icl.cs.utk.edu/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) - Institute for Computational and Mathematical Engineering - Eric Darve

• Start year: 2015

• 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 linear solvers that appear in many intensive numerical simulations in computational sciences. For the solution of large linear systems, the ultimate goal is to design parallel scalable methods that rely on efficient sparse and dense direct methods using H-matrix arithmetic. Finally, the innovative algorithmic design will be essentially focused on heterogeneous manycore platforms by using task based runtime systems. The partners, Inria HiePACS, Lawrence Berkeley Nat. Lab and Stanford University, have strong, complementary and recognized experiences and backgrounds in these fields