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
- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/3633
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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 |
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Comisión de Investigaciones Científicas de la Provincia de Buenos Aires |
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