Using Postordering and Static Symbolic Factorization for Parallel Sparse LU

Michel Cosnard 1 Laura Grigori 1
1 RESEDAS - Software Tools for Telecommunications and Distributed Systems
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper we present several improvements of widely used parallel LU factorization methods on sparse matrices. First we introduce the LU elimination forest and then we characterize the L, U factors in terms of their corresponding LU elimination forest. This characterization can be used as a compact storage scheme of the matrix as well as of the task dependence graph. To improve the use of BLAS in the numerical factorization, we perform a postorder traversal of the LU elimination forest, thus obtaining larger supernodes. To expose more task parallelism for a sparse matrix, we build a more accurate task dependence graph that includes only the least necessary dependences.
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Communication dans un congrès
IEEE International Parallel & Distributed Processing Symposium, May 2000, none, pp.807-812, 2000
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https://hal.inria.fr/inria-00099235
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Soumis le : mardi 26 septembre 2006 - 08:52:01
Dernière modification le : jeudi 11 janvier 2018 - 06:20:00

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  • HAL Id : inria-00099235, version 1

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Michel Cosnard, Laura Grigori. Using Postordering and Static Symbolic Factorization for Parallel Sparse LU. IEEE International Parallel & Distributed Processing Symposium, May 2000, none, pp.807-812, 2000. 〈inria-00099235〉

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