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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 report we present several improvements of widely used parallel LU factorization methods on sparse matrices. First 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 eforest 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 dependencies. Experiments compared favorably our methods against methods implemented in the S* environment on the SGI's Origin2000 multiprocessor.
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https://hal.inria.fr/inria-00069935
Contributor : Rapport de Recherche Inria <>
Submitted on : Friday, May 19, 2006 - 6:37:20 PM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM
Long-term archiving on: : Saturday, April 3, 2010 - 9:42:53 PM

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  • HAL Id : inria-00069935, version 1

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Michel Cosnard, Laura Grigori. Using Postordering and Static Symbolic Factorization for Parallel Sparse LU. [Technical Report] RT-0237, INRIA. 1999, pp.13. ⟨inria-00069935⟩

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