Parallel linear algebra on clusters

Autores
Tinetti, Fernando Gustavo
Año de publicación
2005
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión enviada
Descripción
Parallel performance optimization is being applied and further improvements are studied for parallel linear algebra on clusters. Several parallelization guidelines have been defined and are being used on single clusters and local area networks used for parallel computing. In this context, some linear algebra parallel algorithms have been implemented following the parallelization guidelines, and experimentation has shown very good performance. Also, the parallel algorithms outperform the corresponding parallel algorithms implemented on ScaLAPACK (Scalable LAPACK), which is considered to have highly optimized parallel algorithms for distributed memory parallel computers. Also, using more than a single cluster or local area network for parallel linear algebra computing seems to be a natural approach, taking into account the high availability of such computing platforms in academic/research environments. In this context of multiple clusters, there are many interesting challenges, and many of them are still to be exactly defined and/or characterized. Intercluster communication performance characterization seems to be the first factor to be precisely quantified and it is expected that communication performance quantification will give a starting point from which analyze current and future approaches for parallel performance using more than one cluster or local area network for parallel cooperating processing.
Eje: Otros
Materia
Ciencias Informáticas
parallel linear algebra
clusters
Parallel
Clustering
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:11746/3633

id CICBA_592b2c182cfd08882e85d20bfadc717d
oai_identifier_str oai:digital.cic.gba.gob.ar:11746/3633
network_acronym_str CICBA
repository_id_str 9441
network_name_str CIC Digital (CICBA)
spelling Parallel linear algebra on clustersTinetti, Fernando GustavoCiencias Informáticasparallel linear algebraclustersParallelClusteringParallel performance optimization is being applied and further improvements are studied for parallel linear algebra on clusters. Several parallelization guidelines have been defined and are being used on single clusters and local area networks used for parallel computing. In this context, some linear algebra parallel algorithms have been implemented following the parallelization guidelines, and experimentation has shown very good performance. Also, the parallel algorithms outperform the corresponding parallel algorithms implemented on ScaLAPACK (Scalable LAPACK), which is considered to have highly optimized parallel algorithms for distributed memory parallel computers. Also, using more than a single cluster or local area network for parallel linear algebra computing seems to be a natural approach, taking into account the high availability of such computing platforms in academic/research environments. In this context of multiple clusters, there are many interesting challenges, and many of them are still to be exactly defined and/or characterized. Intercluster communication performance characterization seems to be the first factor to be precisely quantified and it is expected that communication performance quantification will give a starting point from which analyze current and future approaches for parallel performance using more than one cluster or local area network for parallel cooperating processing.Eje: Otros2005-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/submittedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/3633spainfo: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:CICBA2025-09-29T13:40:01Zoai:digital.cic.gba.gob.ar:11746/3633Institucionalhttp://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 13:40:01.745CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse
dc.title.none.fl_str_mv Parallel linear algebra on clusters
title Parallel linear algebra on clusters
spellingShingle Parallel linear algebra on clusters
Tinetti, Fernando Gustavo
Ciencias Informáticas
parallel linear algebra
clusters
Parallel
Clustering
title_short Parallel linear algebra on clusters
title_full Parallel linear algebra on clusters
title_fullStr Parallel linear algebra on clusters
title_full_unstemmed Parallel linear algebra on clusters
title_sort Parallel linear algebra on clusters
dc.creator.none.fl_str_mv Tinetti, Fernando Gustavo
author Tinetti, Fernando Gustavo
author_facet Tinetti, Fernando Gustavo
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
parallel linear algebra
clusters
Parallel
Clustering
topic Ciencias Informáticas
parallel linear algebra
clusters
Parallel
Clustering
dc.description.none.fl_txt_mv Parallel performance optimization is being applied and further improvements are studied for parallel linear algebra on clusters. Several parallelization guidelines have been defined and are being used on single clusters and local area networks used for parallel computing. In this context, some linear algebra parallel algorithms have been implemented following the parallelization guidelines, and experimentation has shown very good performance. Also, the parallel algorithms outperform the corresponding parallel algorithms implemented on ScaLAPACK (Scalable LAPACK), which is considered to have highly optimized parallel algorithms for distributed memory parallel computers. Also, using more than a single cluster or local area network for parallel linear algebra computing seems to be a natural approach, taking into account the high availability of such computing platforms in academic/research environments. In this context of multiple clusters, there are many interesting challenges, and many of them are still to be exactly defined and/or characterized. Intercluster communication performance characterization seems to be the first factor to be precisely quantified and it is expected that communication performance quantification will give a starting point from which analyze current and future approaches for parallel performance using more than one cluster or local area network for parallel cooperating processing.
Eje: Otros
description Parallel performance optimization is being applied and further improvements are studied for parallel linear algebra on clusters. Several parallelization guidelines have been defined and are being used on single clusters and local area networks used for parallel computing. In this context, some linear algebra parallel algorithms have been implemented following the parallelization guidelines, and experimentation has shown very good performance. Also, the parallel algorithms outperform the corresponding parallel algorithms implemented on ScaLAPACK (Scalable LAPACK), which is considered to have highly optimized parallel algorithms for distributed memory parallel computers. Also, using more than a single cluster or local area network for parallel linear algebra computing seems to be a natural approach, taking into account the high availability of such computing platforms in academic/research environments. In this context of multiple clusters, there are many interesting challenges, and many of them are still to be exactly defined and/or characterized. Intercluster communication performance characterization seems to be the first factor to be precisely quantified and it is expected that communication performance quantification will give a starting point from which analyze current and future approaches for parallel performance using more than one cluster or local area network for parallel cooperating processing.
publishDate 2005
dc.date.none.fl_str_mv 2005-05
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/submittedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str submittedVersion
dc.identifier.none.fl_str_mv https://digital.cic.gba.gob.ar/handle/11746/3633
url https://digital.cic.gba.gob.ar/handle/11746/3633
dc.language.none.fl_str_mv spa
language spa
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
_version_ 1844618594151825408
score 13.070432