University of Tennessee (Knoxville, USA), Lawrence Berkeley National Laboratory (Berkeley, USA)
The development of new parallel architectures and accelerators requires the rethinking of most of the numerical linear algebra algorithms which are at the heart of many scientific applications. As a result, innovative methods must be proposed in order to take full advantage of current supercomputers and among them is the use of randomized algorithms. In recent years, techniques based on Random Butterfly Transformations (RBT) have been successfully proposed to accelerate the solution of dense systems. The performance and accuracy results are encouraging on current hybrid multicore/GPU architectures. In this PhD thesis we propose to extend the use of randomized algorithms to sparse (direct and/or iterative) linear system solvers which concern a large class of physical and industrial applications and represent a challenging issue for future exascale systems.
This PhD thesis will take advantage of the new associate-team R-LAS with University of Tennessee (project lead by Marc Baboulin and Jack Dongarra). In the framework of this project, the PhD candidate will benefit from extended visits to Innovative Computing Laboratory (Knoxville, Tennessee), which is one of the world leader in High-Performance Computing.
This PhD thesis aims at developing innovative randomized algorithms and software for sparse matrix computations, either for direct or iterative methods. The resulting software will be applied to large-size simulations addressed by ParSys and its Paris-Saclay partners in the area of high-performance computing (LIMSI, EDF, Centrale Paris). Moreover, for sake of visibility of the results, the software developed during this thesis would be part of a reference public domain library for fast HPC solvers.
Status of funding
Mercredi 26 février 2014 12:31:31 CET
Jeudi 01 mai 2014 15:46:28 CEST
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Ecole Doctorale Informatique Paris-Sud
Nicole Bidoit Assistante
Stéphanie Druetta Conseiller aux thèses
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