Data sparse techniques for parallel hybrid solvers

Abstract : In this talk we will describe how H-matrix data sparse techniques can be implemented in a parallel hybrid sparse linear solver based on algebraic non overlapping domain decomposition approach. Strong-hierarchical matrix arithmetic and various clustering techniques to approximate the local Schur complements will be investigated, aiming at reducing workload and memory consumption while complying with structures of the local interfaces of the sub-domains. Utilization of these techniques to form effective global preconditioner will be presented.
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Communication dans un congrès
SIAM Conference on Applied Linear Algebra (SIAM LA 2015), Apr 2015, Atlanta, United States. 2015
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https://hal.inria.fr/hal-01256230
Contributeur : Luc Giraud <>
Soumis le : jeudi 14 janvier 2016 - 15:16:05
Dernière modification le : jeudi 11 janvier 2018 - 06:22:35

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  • HAL Id : hal-01256230, version 1

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Emmanuel Agullo, Eric Darve, Luc Giraud, Yuval Harness. Data sparse techniques for parallel hybrid solvers. SIAM Conference on Applied Linear Algebra (SIAM LA 2015), Apr 2015, Atlanta, United States. 2015. 〈hal-01256230〉

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