A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm

Autores
Bazterra, Victor E.; Cuma, Martin; Ferraro, Marta Beatriz; Facelli, Julio C.
Año de publicación
2005
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. © 2004 Elsevier Inc. All rights reserved.
Fil: Bazterra, Victor E.. Universidad de Buenos Aires; Argentina. University of Utah; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cuma, Martin. University of Utah; Estados Unidos. Universidad de Buenos Aires; Argentina
Fil: Ferraro, Marta Beatriz. University of Utah; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Facelli, Julio C.. University of Utah; Estados Unidos. Universidad de Buenos Aires; Argentina
Materia
HETEROGENEOUS PARALLEL ENVIRONMENT
PARALLEL GENETIC ALGORITHMS
PERFORMANCE ANALYSIS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/73283
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithmBazterra, Victor E.Cuma, MartinFerraro, Marta BeatrizFacelli, Julio C.HETEROGENEOUS PARALLEL ENVIRONMENTPARALLEL GENETIC ALGORITHMSPERFORMANCE ANALYSIShttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. © 2004 Elsevier Inc. All rights reserved.Fil: Bazterra, Victor E.. Universidad de Buenos Aires; Argentina. University of Utah; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Cuma, Martin. University of Utah; Estados Unidos. Universidad de Buenos Aires; ArgentinaFil: Ferraro, Marta Beatriz. University of Utah; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Facelli, Julio C.. University of Utah; Estados Unidos. Universidad de Buenos Aires; ArgentinaAcademic Press Inc Elsevier Science2005-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/73283Bazterra, Victor E.; Cuma, Martin; Ferraro, Marta Beatriz; Facelli, Julio C.; A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm; Academic Press Inc Elsevier Science; Journal Of Parallel And Distributed Computing; 65; 1; 12-2005; 48-570743-7315CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jpdc.2004.09.011info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0743731504001741info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:54:51Zoai:ri.conicet.gov.ar:11336/73283instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:54:51.723CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
spellingShingle A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
Bazterra, Victor E.
HETEROGENEOUS PARALLEL ENVIRONMENT
PARALLEL GENETIC ALGORITHMS
PERFORMANCE ANALYSIS
title_short A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_full A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_fullStr A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_full_unstemmed A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_sort A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
dc.creator.none.fl_str_mv Bazterra, Victor E.
Cuma, Martin
Ferraro, Marta Beatriz
Facelli, Julio C.
author Bazterra, Victor E.
author_facet Bazterra, Victor E.
Cuma, Martin
Ferraro, Marta Beatriz
Facelli, Julio C.
author_role author
author2 Cuma, Martin
Ferraro, Marta Beatriz
Facelli, Julio C.
author2_role author
author
author
dc.subject.none.fl_str_mv HETEROGENEOUS PARALLEL ENVIRONMENT
PARALLEL GENETIC ALGORITHMS
PERFORMANCE ANALYSIS
topic HETEROGENEOUS PARALLEL ENVIRONMENT
PARALLEL GENETIC ALGORITHMS
PERFORMANCE ANALYSIS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. © 2004 Elsevier Inc. All rights reserved.
Fil: Bazterra, Victor E.. Universidad de Buenos Aires; Argentina. University of Utah; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Cuma, Martin. University of Utah; Estados Unidos. Universidad de Buenos Aires; Argentina
Fil: Ferraro, Marta Beatriz. University of Utah; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Facelli, Julio C.. University of Utah; Estados Unidos. Universidad de Buenos Aires; Argentina
description This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. © 2004 Elsevier Inc. All rights reserved.
publishDate 2005
dc.date.none.fl_str_mv 2005-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/73283
Bazterra, Victor E.; Cuma, Martin; Ferraro, Marta Beatriz; Facelli, Julio C.; A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm; Academic Press Inc Elsevier Science; Journal Of Parallel And Distributed Computing; 65; 1; 12-2005; 48-57
0743-7315
CONICET Digital
CONICET
url http://hdl.handle.net/11336/73283
identifier_str_mv Bazterra, Victor E.; Cuma, Martin; Ferraro, Marta Beatriz; Facelli, Julio C.; A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm; Academic Press Inc Elsevier Science; Journal Of Parallel And Distributed Computing; 65; 1; 12-2005; 48-57
0743-7315
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jpdc.2004.09.011
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0743731504001741
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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