Sparse Equation Systems in Heterogeneous Clusters of Computers

Autores
Tinetti, Fernando Gustavo; Aróztegui, Walter; Quijano, Antonio A.
Año de publicación
2005
Idioma
inglés
Tipo de recurso
informe técnico
Estado
versión enviada
Descripción
This paper presents a parallelization strategy in heterogeneous clusters of the Gauss-Seidel’s method applied for the solution of sparse equation systems. From the point of view of the numerical solution for matrices of coefficients with low density of non null-elements, the standard lines of thought are followed, that is, only non-null elements are stored and iterative solution-search methods are used. Two basic guidelines are defined for the parallel algorithm: one-dimensional data distribution and broadcast messages for all data communications. One-dimensional data distribution eases the processing workload balance on heterogeneous clusters. The use of broadcast messages for every data communication is directly oriented to optimize performance on the the most common cluster interconnection: Ethernet. Experimental results obtained in a local network of heterogeneous computers are presented.
Materia
Ingeniería Eléctrica y Electrónica
heterogeneous clusters
Gauss-Seidel’s method
sparse equation systems
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
CIC Digital (CICBA)
Institución
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
OAI Identificador
oai:digital.cic.gba.gob.ar:11746/6514

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repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Sparse Equation Systems in Heterogeneous Clusters of ComputersTinetti, Fernando GustavoAróztegui, WalterQuijano, Antonio A.Ingeniería Eléctrica y Electrónicaheterogeneous clustersGauss-Seidel’s methodsparse equation systemsThis paper presents a parallelization strategy in heterogeneous clusters of the Gauss-Seidel’s method applied for the solution of sparse equation systems. From the point of view of the numerical solution for matrices of coefficients with low density of non null-elements, the standard lines of thought are followed, that is, only non-null elements are stored and iterative solution-search methods are used. Two basic guidelines are defined for the parallel algorithm: one-dimensional data distribution and broadcast messages for all data communications. One-dimensional data distribution eases the processing workload balance on heterogeneous clusters. The use of broadcast messages for every data communication is directly oriented to optimize performance on the the most common cluster interconnection: Ethernet. Experimental results obtained in a local network of heterogeneous computers are presented.2005info:eu-repo/semantics/reportinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_18ghinfo:ar-repo/semantics/informeTecnicoapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/6514enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-09-29T13:40:22Zoai:digital.cic.gba.gob.ar:11746/6514Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-09-29 13:40:22.328CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Sparse Equation Systems in Heterogeneous Clusters of Computers
title Sparse Equation Systems in Heterogeneous Clusters of Computers
spellingShingle Sparse Equation Systems in Heterogeneous Clusters of Computers
Tinetti, Fernando Gustavo
Ingeniería Eléctrica y Electrónica
heterogeneous clusters
Gauss-Seidel’s method
sparse equation systems
title_short Sparse Equation Systems in Heterogeneous Clusters of Computers
title_full Sparse Equation Systems in Heterogeneous Clusters of Computers
title_fullStr Sparse Equation Systems in Heterogeneous Clusters of Computers
title_full_unstemmed Sparse Equation Systems in Heterogeneous Clusters of Computers
title_sort Sparse Equation Systems in Heterogeneous Clusters of Computers
dc.creator.none.fl_str_mv Tinetti, Fernando Gustavo
Aróztegui, Walter
Quijano, Antonio A.
author Tinetti, Fernando Gustavo
author_facet Tinetti, Fernando Gustavo
Aróztegui, Walter
Quijano, Antonio A.
author_role author
author2 Aróztegui, Walter
Quijano, Antonio A.
author2_role author
author
dc.subject.none.fl_str_mv Ingeniería Eléctrica y Electrónica
heterogeneous clusters
Gauss-Seidel’s method
sparse equation systems
topic Ingeniería Eléctrica y Electrónica
heterogeneous clusters
Gauss-Seidel’s method
sparse equation systems
dc.description.none.fl_txt_mv This paper presents a parallelization strategy in heterogeneous clusters of the Gauss-Seidel’s method applied for the solution of sparse equation systems. From the point of view of the numerical solution for matrices of coefficients with low density of non null-elements, the standard lines of thought are followed, that is, only non-null elements are stored and iterative solution-search methods are used. Two basic guidelines are defined for the parallel algorithm: one-dimensional data distribution and broadcast messages for all data communications. One-dimensional data distribution eases the processing workload balance on heterogeneous clusters. The use of broadcast messages for every data communication is directly oriented to optimize performance on the the most common cluster interconnection: Ethernet. Experimental results obtained in a local network of heterogeneous computers are presented.
description This paper presents a parallelization strategy in heterogeneous clusters of the Gauss-Seidel’s method applied for the solution of sparse equation systems. From the point of view of the numerical solution for matrices of coefficients with low density of non null-elements, the standard lines of thought are followed, that is, only non-null elements are stored and iterative solution-search methods are used. Two basic guidelines are defined for the parallel algorithm: one-dimensional data distribution and broadcast messages for all data communications. One-dimensional data distribution eases the processing workload balance on heterogeneous clusters. The use of broadcast messages for every data communication is directly oriented to optimize performance on the the most common cluster interconnection: Ethernet. Experimental results obtained in a local network of heterogeneous computers are presented.
publishDate 2005
dc.date.none.fl_str_mv 2005
dc.type.none.fl_str_mv info:eu-repo/semantics/report
info:eu-repo/semantics/submittedVersion
http://purl.org/coar/resource_type/c_18gh
info:ar-repo/semantics/informeTecnico
format report
status_str submittedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/6514
url https://digital.cic.gba.gob.ar/handle/11746/6514
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:CIC Digital (CICBA)
instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
reponame_str CIC Digital (CICBA)
collection CIC Digital (CICBA)
instname_str Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron_str CICBA
institution CICBA
repository.name.fl_str_mv CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
repository.mail.fl_str_mv marisa.degiusti@sedici.unlp.edu.ar
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