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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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Submitted on : Tuesday, September 26, 2006 - 8:52:01 AM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM


  • HAL Id : inria-00099235, version 1



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. ⟨inria-00099235⟩



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