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The Journal of Supercomputing
Published in: The Journal of Supercomputing 1/2014

01-07-2014

Parallel sparse linear solver with GMRES method using minimization techniques of communications for GPU clusters

Authors: Lilia Ziane Khodja, Raphaël Couturier, Arnaud Giersch, Jacques M. Bahi

Published in: The Journal of Supercomputing | Issue 1/2014

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Abstract

In this paper, we aim at exploiting the power computing of a graphics processing unit (GPU) cluster for solving large sparse linear systems. We implement the parallel algorithm of the generalized minimal residual iterative method using the Compute Unified Device Architecture programming language and the MPI parallel environment. The experiments show that a GPU cluster is more efficient than a CPU cluster. In order to optimize the performances, we use a compressed storage format for the sparse vectors and the hypergraph partitioning. These solutions improve the spatial and temporal localization of the shared data between the computing nodes of the GPU cluster.
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Metadata
Title
Parallel sparse linear solver with GMRES method using minimization techniques of communications for GPU clusters
Authors
Lilia Ziane Khodja
Raphaël Couturier
Arnaud Giersch
Jacques M. Bahi
Publication date
01-07-2014
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 1/2014
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-014-1143-8

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