A comparative study on dynamic and static sparsity patterns in parallel sparse approximate inverse preconditioning

Kai Wang; Sangbae Kim; Jun Zhang

doi:10.1023/B:JMMA.0000015831.64190.1e

A comparative study on dynamic and static sparsity patterns in parallel sparse approximate inverse preconditioning

Kai Wang, Sangbae Kim, Jun Zhang

Computer Science

Producción científica: Article › revisión exhaustiva

4 Citas (Scopus)

Resumen

Sparse approximate inverse (SAI) techniques have recently emerged as a new class of parallel preconditioning techniques for solving large sparse linear systems on high performance computers. The choice of the sparsity pattern of the SAI matrix is probably the most important step in constructing an SAI preconditioner. Both dynamic and static sparsity pattern selection approaches have been proposed by researchers. Through a few numerical experiments, we conduct a comparable study on the properties and performance of the SAI preconditioners using the different sparsity patterns for solving some sparse linear systems.

Idioma original	English
Páginas (desde-hasta)	203-215
Número de páginas	13
Publicación	Journal of Mathematical Modelling and Algorithms
Volumen	2
N.º	3
DOI	https://doi.org/10.1023/B:JMMA.0000015831.64190.1e
Estado	Published - 2003

Nota bibliográfica

Funding Information:
★ This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science & Technology, and in part by the University of Kentucky Research Committee.

Financiación

\u2605 This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science & Technology, and in part by the University of Kentucky Research Committee.

Financiadores	Número del financiador
Japan Research Organization for Information Science and Technology
University of Kentucky Research Committee
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China	CCR-0092532, CCR-9902022, ACI-0202934, 0202934, CCR-9988165

ASJC Scopus subject areas

Modeling and Simulation
Applied Mathematics

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10.1023/B:JMMA.0000015831.64190.1e

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title = "A comparative study on dynamic and static sparsity patterns in parallel sparse approximate inverse preconditioning",

abstract = "Sparse approximate inverse (SAI) techniques have recently emerged as a new class of parallel preconditioning techniques for solving large sparse linear systems on high performance computers. The choice of the sparsity pattern of the SAI matrix is probably the most important step in constructing an SAI preconditioner. Both dynamic and static sparsity pattern selection approaches have been proposed by researchers. Through a few numerical experiments, we conduct a comparable study on the properties and performance of the SAI preconditioners using the different sparsity patterns for solving some sparse linear systems.",

keywords = "Dynamic and static sparsity pattern, Parallel preconditioning, Sparse approximate inverse, Sparse matrices",

author = "Kai Wang and Sangbae Kim and Jun Zhang",

note = "Funding Information: ★ This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science \& Technology, and in part by the University of Kentucky Research Committee.",

year = "2003",

doi = "10.1023/B:JMMA.0000015831.64190.1e",

language = "English",

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T1 - A comparative study on dynamic and static sparsity patterns in parallel sparse approximate inverse preconditioning

AU - Wang, Kai

AU - Kim, Sangbae

AU - Zhang, Jun

N1 - Funding Information: ★ This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science & Technology, and in part by the University of Kentucky Research Committee.

PY - 2003

Y1 - 2003

N2 - Sparse approximate inverse (SAI) techniques have recently emerged as a new class of parallel preconditioning techniques for solving large sparse linear systems on high performance computers. The choice of the sparsity pattern of the SAI matrix is probably the most important step in constructing an SAI preconditioner. Both dynamic and static sparsity pattern selection approaches have been proposed by researchers. Through a few numerical experiments, we conduct a comparable study on the properties and performance of the SAI preconditioners using the different sparsity patterns for solving some sparse linear systems.

AB - Sparse approximate inverse (SAI) techniques have recently emerged as a new class of parallel preconditioning techniques for solving large sparse linear systems on high performance computers. The choice of the sparsity pattern of the SAI matrix is probably the most important step in constructing an SAI preconditioner. Both dynamic and static sparsity pattern selection approaches have been proposed by researchers. Through a few numerical experiments, we conduct a comparable study on the properties and performance of the SAI preconditioners using the different sparsity patterns for solving some sparse linear systems.

KW - Dynamic and static sparsity pattern

KW - Parallel preconditioning

KW - Sparse approximate inverse

KW - Sparse matrices

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A comparative study on dynamic and static sparsity patterns in parallel sparse approximate inverse preconditioning

Resumen

Nota bibliográfica

Financiación

ASJC Scopus subject areas

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