Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning

The present paper describes a parallel preconditioned algorithm for the solution of partial eigenvalue problems for large sparse symmetric matrices, on parallel computers. Namely, we consider the Deflation-Accelerated Conjugate Gradient (DACG) algorithm accelerated by factorized-sparse-approximate-i...

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Main Authors: Luca Bergamaschi, Angeles Martínez, Giorgio Pini
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2012/872901
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author Luca Bergamaschi
Angeles Martínez
Giorgio Pini
author_facet Luca Bergamaschi
Angeles Martínez
Giorgio Pini
author_sort Luca Bergamaschi
collection DOAJ
description The present paper describes a parallel preconditioned algorithm for the solution of partial eigenvalue problems for large sparse symmetric matrices, on parallel computers. Namely, we consider the Deflation-Accelerated Conjugate Gradient (DACG) algorithm accelerated by factorized-sparse-approximate-inverse- (FSAI-) type preconditioners. We present an enhanced parallel implementation of the FSAI preconditioner and make use of the recently developed Block FSAI-IC preconditioner, which combines the FSAI and the Block Jacobi-IC preconditioners. Results onto matrices of large size arising from finite element discretization of geomechanical models reveal that DACG accelerated by these type of preconditioners is competitive with respect to the available public parallel hypre package, especially in the computation of a few of the leftmost eigenpairs. The parallel DACG code accelerated by FSAI is written in MPI-Fortran 90 language and exhibits good scalability up to one thousand processors.
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spelling doaj-art-c931e907048745e7823eb373649ac83c2025-02-03T01:10:51ZengWileyJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/872901872901Parallel Rayleigh Quotient Optimization with FSAI-Based PreconditioningLuca Bergamaschi0Angeles Martínez1Giorgio Pini2Department of Mathematical Methods and Models for Scientific Applications, University of Padova, Via Trieste 63, 35121 Padova, ItalyDepartment of Mathematical Methods and Models for Scientific Applications, University of Padova, Via Trieste 63, 35121 Padova, ItalyDepartment of Mathematical Methods and Models for Scientific Applications, University of Padova, Via Trieste 63, 35121 Padova, ItalyThe present paper describes a parallel preconditioned algorithm for the solution of partial eigenvalue problems for large sparse symmetric matrices, on parallel computers. Namely, we consider the Deflation-Accelerated Conjugate Gradient (DACG) algorithm accelerated by factorized-sparse-approximate-inverse- (FSAI-) type preconditioners. We present an enhanced parallel implementation of the FSAI preconditioner and make use of the recently developed Block FSAI-IC preconditioner, which combines the FSAI and the Block Jacobi-IC preconditioners. Results onto matrices of large size arising from finite element discretization of geomechanical models reveal that DACG accelerated by these type of preconditioners is competitive with respect to the available public parallel hypre package, especially in the computation of a few of the leftmost eigenpairs. The parallel DACG code accelerated by FSAI is written in MPI-Fortran 90 language and exhibits good scalability up to one thousand processors.http://dx.doi.org/10.1155/2012/872901
spellingShingle Luca Bergamaschi
Angeles Martínez
Giorgio Pini
Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning
Journal of Applied Mathematics
title Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning
title_full Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning
title_fullStr Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning
title_full_unstemmed Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning
title_short Parallel Rayleigh Quotient Optimization with FSAI-Based Preconditioning
title_sort parallel rayleigh quotient optimization with fsai based preconditioning
url http://dx.doi.org/10.1155/2012/872901
work_keys_str_mv AT lucabergamaschi parallelrayleighquotientoptimizationwithfsaibasedpreconditioning
AT angelesmartinez parallelrayleighquotientoptimizationwithfsaibasedpreconditioning
AT giorgiopini parallelrayleighquotientoptimizationwithfsaibasedpreconditioning