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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9686
Ver los metadatos del registro completo
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 |