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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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Contributor : Luc Giraud Connect in order to contact the contributor
Submitted on : Thursday, January 14, 2016 - 3:16:05 PM
Last modification on : Friday, November 18, 2022 - 9:27:05 AM


  • HAL Id : hal-01256230, version 1


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. ⟨hal-01256230⟩



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