Robust parallel ILU preconditioning techniques for solving large sparse matrices

Chi Shen; Jun Zhang

doi:10.1109/IPDPS.2002.1016646

Robust parallel ILU preconditioning techniques for solving large sparse matrices

Chi Shen, Jun Zhang

Computer Science

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

3 Scopus citations

Abstract

We discuss issues related to domain decomposition and multilevel preconditioning techniques which are often employed for solving large sparse linear systems in parallel computations. We implement a parallel preconditioner for solving general sparse linear systems based on a two level block ILU factorization strategy. We give some new data structures and strategies to construct a local coefficient matrix and a local Schur complement matrix on each processor. The preconditioner constructed is fast and robust for solving certain large sparse matrices. Numerical experiments show that our domain based two level block ILU preconditioners are more robust and more efficient than some published ILU preconditioners based on Schur complement techniques for parallel sparse matrix solutions.

Original language	English
Title of host publication	Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002
Pages	231
Number of pages	1
ISBN (Electronic)	0769515738, 9780769515731
DOIs	https://doi.org/10.1109/IPDPS.2002.1016646
State	Published - 2002
Event	16th International Parallel and Distributed Processing Symposium, IPDPS 2002 - Ft. Lauderdale, United States Duration: Apr 15 2002 → Apr 19 2002

Publication series

Name	Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002

Conference

Conference	16th International Parallel and Distributed Processing Symposium, IPDPS 2002
Country/Territory	United States
City	Ft. Lauderdale
Period	4/15/02 → 4/19/02

Bibliographical note

Publisher Copyright:
© 2002 IEEE.

ASJC Scopus subject areas

Computer Networks and Communications
Modeling and Simulation

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10.1109/IPDPS.2002.1016646

Cite this

@inproceedings{11a4ed1341144abb89415d5ee238c22f,

title = "Robust parallel ILU preconditioning techniques for solving large sparse matrices",

abstract = "We discuss issues related to domain decomposition and multilevel preconditioning techniques which are often employed for solving large sparse linear systems in parallel computations. We implement a parallel preconditioner for solving general sparse linear systems based on a two level block ILU factorization strategy. We give some new data structures and strategies to construct a local coefficient matrix and a local Schur complement matrix on each processor. The preconditioner constructed is fast and robust for solving certain large sparse matrices. Numerical experiments show that our domain based two level block ILU preconditioners are more robust and more efficient than some published ILU preconditioners based on Schur complement techniques for parallel sparse matrix solutions.",

author = "Chi Shen and Jun Zhang",

note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; 16th International Parallel and Distributed Processing Symposium, IPDPS 2002 ; Conference date: 15-04-2002 Through 19-04-2002",

year = "2002",

doi = "10.1109/IPDPS.2002.1016646",

language = "English",

series = "Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002",

pages = "231",

booktitle = "Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002",

}

Shen, C & Zhang, J 2002, Robust parallel ILU preconditioning techniques for solving large sparse matrices. in Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002., 1016646, Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002, pp. 231, 16th International Parallel and Distributed Processing Symposium, IPDPS 2002, Ft. Lauderdale, United States, 4/15/02. https://doi.org/10.1109/IPDPS.2002.1016646

Robust parallel ILU preconditioning techniques for solving large sparse matrices. / Shen, Chi; Zhang, Jun.
Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002. 2002. p. 231 1016646 (Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002).

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

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N2 - We discuss issues related to domain decomposition and multilevel preconditioning techniques which are often employed for solving large sparse linear systems in parallel computations. We implement a parallel preconditioner for solving general sparse linear systems based on a two level block ILU factorization strategy. We give some new data structures and strategies to construct a local coefficient matrix and a local Schur complement matrix on each processor. The preconditioner constructed is fast and robust for solving certain large sparse matrices. Numerical experiments show that our domain based two level block ILU preconditioners are more robust and more efficient than some published ILU preconditioners based on Schur complement techniques for parallel sparse matrix solutions.

AB - We discuss issues related to domain decomposition and multilevel preconditioning techniques which are often employed for solving large sparse linear systems in parallel computations. We implement a parallel preconditioner for solving general sparse linear systems based on a two level block ILU factorization strategy. We give some new data structures and strategies to construct a local coefficient matrix and a local Schur complement matrix on each processor. The preconditioner constructed is fast and robust for solving certain large sparse matrices. Numerical experiments show that our domain based two level block ILU preconditioners are more robust and more efficient than some published ILU preconditioners based on Schur complement techniques for parallel sparse matrix solutions.

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BT - Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2002

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Robust parallel ILU preconditioning techniques for solving large sparse matrices

Abstract

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Conference

Bibliographical note

ASJC Scopus subject areas

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