Parallel Acceleration of Krylov Solvers by Factorized Approximate Inverse Preconditioners

This paper describes and tests a parallel implementation of a factorized approximate inverse preconditioner (FSAI) to accelerate iterative linear system solvers. Such a preconditioner reveals an efficient accelerator of both Conjugate gradient and BiCGstab iterative methods in the parallel solution...

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Published inHigh Performance Computing for Computational Science - VECPAR 2004 pp. 623 - 636
Main Authors Bergamaschi, Luca, Martínez, Ángeles
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Approximate Inverse
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Diagonal Preconditioner
Exact sciences and technology
Nonsymmetric Case
Nonsymmetric Linear System
Software
Sparsity Pattern
Conjugate gradient method
High performance
Parallel algorithm
Krylov subspace method
Iterative method
Advection diffusion equation
Distributed computing
Discretization
Message passing
Sparse matrix
Gradient method
Preconditioning
Accelerator
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ISBN9783540254249
3540254242
ISSN0302-9743
1611-3349
DOI10.1007/11403937_47

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Abstract This paper describes and tests a parallel implementation of a factorized approximate inverse preconditioner (FSAI) to accelerate iterative linear system solvers. Such a preconditioner reveals an efficient accelerator of both Conjugate gradient and BiCGstab iterative methods in the parallel solution of large linear systems arising from the discretization of the advection-diffusion equation. The resulting message passing code allows the solution of large problems leading to a very cost-effective algorithm for the solution of large and sparse linear systems.
AbstractList This paper describes and tests a parallel implementation of a factorized approximate inverse preconditioner (FSAI) to accelerate iterative linear system solvers. Such a preconditioner reveals an efficient accelerator of both Conjugate gradient and BiCGstab iterative methods in the parallel solution of large linear systems arising from the discretization of the advection-diffusion equation. The resulting message passing code allows the solution of large problems leading to a very cost-effective algorithm for the solution of large and sparse linear systems.
Author Martínez, Ángeles
Bergamaschi, Luca
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  givenname: Ángeles
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  organization: Dipartimento di Metodi e Modelli Matematici per le Scienze Applicate, Università di Padova, Padova, Italy
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2005 INIST-CNRS
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Editor Daydé, Michel
Hernández, Vicente
Dongarra, Jack
Palma, José M. L. M.
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Keywords Conjugate gradient method
High performance
Parallel algorithm
Krylov subspace method
Iterative method
Advection diffusion equation
Distributed computing
Discretization
Message passing
Sparse matrix
Gradient method
Preconditioning
Accelerator
Language English
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MeetingName High performance computing for computational science (Valencia, 28-30 june 2004, revised selected and invited papers)
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PublicationSeriesTitle Lecture Notes in Computer Science
PublicationSubtitle 6th International Conference, Valencia, Spain, June 28-30, 2004, Revised Selected and Invited Papers
PublicationTitle High Performance Computing for Computational Science - VECPAR 2004
PublicationYear 2005
Publisher Springer Berlin Heidelberg
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Mattern, Friedemann
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Vardi, Moshe Y.
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Snippet This paper describes and tests a parallel implementation of a factorized approximate inverse preconditioner (FSAI) to accelerate iterative linear system...
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StartPage 623
SubjectTerms Applied sciences
Approximate Inverse
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Diagonal Preconditioner
Exact sciences and technology
Nonsymmetric Case
Nonsymmetric Linear System
Software
Sparsity Pattern
Title Parallel Acceleration of Krylov Solvers by Factorized Approximate Inverse Preconditioners
URI http://link.springer.com/10.1007/11403937_47
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