Toward memory-efficient linear solvers

Allison Baker; John Dennis; Elizabeth R. Jessup

doi:10.1007/3-540-36569-9_20

Toward memory-efficient linear solvers

Allison Baker, John Dennis, Elizabeth R. Jessup

Application Scalability and Performance Group

University of Colorado Boulder

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

3 Scopus citations

Abstract

We describe a new technique for solving a sparse linear system Ax = b as a block system AX = B, where multiple starting vectors and right-hand sides are chosen so as to accelerate convergence. Efficiency is gained by reusing the matrix A in block operations with X and B. Techniques for reducing the cost of the extra matrix-vector operations are presented.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Jose M. L. M. Palma, A. Augusto Sousa, Jack Dongarra, Vicente Hernandez
Publisher	Springer Verlag
Pages	315-327
Number of pages	13
ISBN (Print)	3540008527, 9783540008521
DOIs	https://doi.org/10.1007/3-540-36569-9_20
State	Published - 2003

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2565
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/3-540-36569-9_20

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Baker, A., Dennis, J., & Jessup, E. R. (2003). Toward memory-efficient linear solvers. In J. M. L. M. Palma, A. Augusto Sousa, J. Dongarra, & V. Hernandez (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 315-327). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2565). Springer Verlag. https://doi.org/10.1007/3-540-36569-9_20

Baker, Allison ; Dennis, John ; Jessup, Elizabeth R. / Toward memory-efficient linear solvers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Jose M. L. M. Palma ; A. Augusto Sousa ; Jack Dongarra ; Vicente Hernandez. Springer Verlag, 2003. pp. 315-327 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Baker, A , Dennis, J & Jessup, ER 2003, Toward memory-efficient linear solvers. in JMLM Palma, A Augusto Sousa, J Dongarra & V Hernandez (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2565, Springer Verlag, pp. 315-327. https://doi.org/10.1007/3-540-36569-9_20

Toward memory-efficient linear solvers. / Baker, Allison ; Dennis, John; Jessup, Elizabeth R.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Jose M. L. M. Palma; A. Augusto Sousa; Jack Dongarra; Vicente Hernandez. Springer Verlag, 2003. p. 315-327 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2565).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Toward memory-efficient linear solvers

AU - Baker, Allison

AU - Dennis, John

AU - Jessup, Elizabeth R.

PY - 2003

Y1 - 2003

N2 - We describe a new technique for solving a sparse linear system Ax = b as a block system AX = B, where multiple starting vectors and right-hand sides are chosen so as to accelerate convergence. Efficiency is gained by reusing the matrix A in block operations with X and B. Techniques for reducing the cost of the extra matrix-vector operations are presented.

AB - We describe a new technique for solving a sparse linear system Ax = b as a block system AX = B, where multiple starting vectors and right-hand sides are chosen so as to accelerate convergence. Efficiency is gained by reusing the matrix A in block operations with X and B. Techniques for reducing the cost of the extra matrix-vector operations are presented.

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DO - 10.1007/3-540-36569-9_20

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SN - 3540008527

SN - 9783540008521

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

A2 - Palma, Jose M. L. M.

A2 - Augusto Sousa, A.

A2 - Dongarra, Jack

A2 - Hernandez, Vicente

PB - Springer Verlag

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Baker A , Dennis J, Jessup ER. Toward memory-efficient linear solvers. In Palma JMLM, Augusto Sousa A, Dongarra J, Hernandez V, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2003. p. 315-327. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-36569-9_20

Toward memory-efficient linear solvers

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