Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing

Autores
Wong, Alvaro
Año de publicación
2010
Idioma
inglés
Tipo de recurso
reseña artículo
Estado
versión publicada
Descripción
In order to measure the performance of a parallel machine, a set of application kernels as benchmarks have often been used. However, it is not always possible to characterize the performance using only benchmarks, given the fact that each one usually reflects a narrow set of kernel applications at best. Computers show different performance indices for different applications as they run them. Accurate prediction of parallel applications’ performance is becoming increasingly complex and the time required to run it thoroughly is an onerous requirement; especially if we want to predict for different systems. In production clusters, where throughput and efficiency of use are fundamental, it is important to be able to predict which system is more appropriate for an application, or how long a scheduled application will take to run, in order to have the foresight that will allow us to make better use of the resources available.
Facultad de Informática
Materia
Ciencias Informáticas
Parallel
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9686

id SEDICI_d9c369d3f44aa76ef3c304736e60bfd1
oai_identifier_str oai:sedici.unlp.edu.ar:10915/9686
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance ComputingWong, AlvaroCiencias InformáticasParallelIn order to measure the performance of a parallel machine, a set of application kernels as benchmarks have often been used. However, it is not always possible to characterize the performance using only benchmarks, given the fact that each one usually reflects a narrow set of kernel applications at best. Computers show different performance indices for different applications as they run them. Accurate prediction of parallel applications’ performance is becoming increasingly complex and the time required to run it thoroughly is an onerous requirement; especially if we want to predict for different systems. In production clusters, where throughput and efficiency of use are fundamental, it is important to be able to predict which system is more appropriate for an application, or how long a scheduled application will take to run, in order to have the foresight that will allow us to make better use of the resources available.Facultad de Informática2010-10info:eu-repo/semantics/reviewinfo:eu-repo/semantics/publishedVersionRevisionhttp://purl.org/coar/resource_type/c_dcae04bcinfo:ar-repo/semantics/resenaArticuloapplication/pdf155-156http://sedici.unlp.edu.ar/handle/10915/9686enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct10-TO3.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:48Zoai:sedici.unlp.edu.ar:10915/9686Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:48.979SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
title Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
spellingShingle Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
Wong, Alvaro
Ciencias Informáticas
Parallel
title_short Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
title_full Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
title_fullStr Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
title_full_unstemmed Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
title_sort Parallel Application Signature for Performance Prediction : Ph. D. Thesis in High Perfomance Computing
dc.creator.none.fl_str_mv Wong, Alvaro
author Wong, Alvaro
author_facet Wong, Alvaro
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Parallel
topic Ciencias Informáticas
Parallel
dc.description.none.fl_txt_mv In order to measure the performance of a parallel machine, a set of application kernels as benchmarks have often been used. However, it is not always possible to characterize the performance using only benchmarks, given the fact that each one usually reflects a narrow set of kernel applications at best. Computers show different performance indices for different applications as they run them. Accurate prediction of parallel applications’ performance is becoming increasingly complex and the time required to run it thoroughly is an onerous requirement; especially if we want to predict for different systems. In production clusters, where throughput and efficiency of use are fundamental, it is important to be able to predict which system is more appropriate for an application, or how long a scheduled application will take to run, in order to have the foresight that will allow us to make better use of the resources available.
Facultad de Informática
description In order to measure the performance of a parallel machine, a set of application kernels as benchmarks have often been used. However, it is not always possible to characterize the performance using only benchmarks, given the fact that each one usually reflects a narrow set of kernel applications at best. Computers show different performance indices for different applications as they run them. Accurate prediction of parallel applications’ performance is becoming increasingly complex and the time required to run it thoroughly is an onerous requirement; especially if we want to predict for different systems. In production clusters, where throughput and efficiency of use are fundamental, it is important to be able to predict which system is more appropriate for an application, or how long a scheduled application will take to run, in order to have the foresight that will allow us to make better use of the resources available.
publishDate 2010
dc.date.none.fl_str_mv 2010-10
dc.type.none.fl_str_mv info:eu-repo/semantics/review
info:eu-repo/semantics/publishedVersion
Revision
http://purl.org/coar/resource_type/c_dcae04bc
info:ar-repo/semantics/resenaArticulo
format review
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9686
url http://sedici.unlp.edu.ar/handle/10915/9686
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct10-TO3.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
155-156
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
_version_ 1844615758835875840
score 13.070432