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Block Sparse Cholesky Algorithms on Advanced Uniprocessor Computers

Published: 01 September 1993 Publication History

Abstract

As with many other linear algebra algorithms, devising a portable implementation of sparse Cholesky factorization that performs well on the broad range of computer architectures currently available is a formidable challenge. Even after limiting the attention to machines with only one processor, as has been done in this paper, there are still several interesting issues to consider. For dense matrices, it is well known that block factorization algorithms are the best means of achieving this goal. This approach is taken for sparse factorization as well.
This paper has two primary goals. First, two sparse Cholesky factorization algorithms, the multifrontal method and a blocked left-looking sparse Cholesky method, are examined in a systematic and consistent fashion, both to illustrate the strengths of the blocking techniques in general and to obtain a fair evaluation of the two approaches, Second, the impact of various implementation techniques on time and storage efficiency is assessed, paying particularly close attention to the work-storage requirement of the two methods and their variants.

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Information & Contributors

Information

Published In

cover image SIAM Journal on Scientific Computing
SIAM Journal on Scientific Computing  Volume 14, Issue 5
Sep 1993
245 pages

Publisher

Society for Industrial and Applied Mathematics

United States

Publication History

Published: 01 September 1993

Author Tags

  1. 65F
  2. 65W

Author Tags

  1. sparse linear systems
  2. Cholesky factorization
  3. supernodes
  4. block algorithms
  5. advanced computer architectures

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  • (2018)A Left-Looking Selected Inversion Algorithm and Task Parallelism on Shared Memory SystemsProceedings of the International Conference on High Performance Computing in Asia-Pacific Region10.1145/3149457.3149472(54-63)Online publication date: 28-Jan-2018
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