Towards a Strategy for Performance Prediction on Heterogeneous Architectures

Detalhes bibliográficos
Autor(a) principal: Stanzani, Silvio [UNESP]
Data de Publicação: 2019
Outros Autores: Cóbe, Raphael [UNESP], Fialho, Jefferson [UNESP], Iope, Rogério [UNESP], Gomes, Marco [UNESP], Baruchi, Artur [UNESP], Amaral, Júlio [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-030-15996-2_18
http://hdl.handle.net/11449/221286
Resumo: Performance prediction of applications has always been a great challenge, even for homogeneous architectures. However, today’s trend is the design of cluster running in a heterogeneous architecture, which increases the complexity of new strategies to predict the behavior and time spent by an application to run. In this paper we present a strategy that predicts the performance of an application on different architectures and rank then according to the performance that the application can achieve on each architecture. The proposed strategy was able to correctly rank three of four applications tested without overhead implications. Our next step is to extend the metrics in order to increase the accuracy.
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spelling Towards a Strategy for Performance Prediction on Heterogeneous ArchitecturesHeterogeneous systemsParallel processingPerformance predictionPerformance prediction of applications has always been a great challenge, even for homogeneous architectures. However, today’s trend is the design of cluster running in a heterogeneous architecture, which increases the complexity of new strategies to predict the behavior and time spent by an application to run. In this paper we present a strategy that predicts the performance of an application on different architectures and rank then according to the performance that the application can achieve on each architecture. The proposed strategy was able to correctly rank three of four applications tested without overhead implications. Our next step is to extend the metrics in order to increase the accuracy.Núcleo de Computação Científica (NCC) Universidade Estadual PaulistaNúcleo de Computação Científica (NCC) Universidade Estadual PaulistaUniversidade Estadual Paulista (UNESP)Stanzani, Silvio [UNESP]Cóbe, Raphael [UNESP]Fialho, Jefferson [UNESP]Iope, Rogério [UNESP]Gomes, Marco [UNESP]Baruchi, Artur [UNESP]Amaral, Júlio [UNESP]2022-04-28T19:27:08Z2022-04-28T19:27:08Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject247-253http://dx.doi.org/10.1007/978-3-030-15996-2_18Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11333 LNCS, p. 247-253.1611-33490302-9743http://hdl.handle.net/11449/22128610.1007/978-3-030-15996-2_182-s2.0-85064598015Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2022-04-28T19:27:08Zoai:repositorio.unesp.br:11449/221286Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-28T19:27:08Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Towards a Strategy for Performance Prediction on Heterogeneous Architectures
title Towards a Strategy for Performance Prediction on Heterogeneous Architectures
spellingShingle Towards a Strategy for Performance Prediction on Heterogeneous Architectures
Stanzani, Silvio [UNESP]
Heterogeneous systems
Parallel processing
Performance prediction
title_short Towards a Strategy for Performance Prediction on Heterogeneous Architectures
title_full Towards a Strategy for Performance Prediction on Heterogeneous Architectures
title_fullStr Towards a Strategy for Performance Prediction on Heterogeneous Architectures
title_full_unstemmed Towards a Strategy for Performance Prediction on Heterogeneous Architectures
title_sort Towards a Strategy for Performance Prediction on Heterogeneous Architectures
author Stanzani, Silvio [UNESP]
author_facet Stanzani, Silvio [UNESP]
Cóbe, Raphael [UNESP]
Fialho, Jefferson [UNESP]
Iope, Rogério [UNESP]
Gomes, Marco [UNESP]
Baruchi, Artur [UNESP]
Amaral, Júlio [UNESP]
author_role author
author2 Cóbe, Raphael [UNESP]
Fialho, Jefferson [UNESP]
Iope, Rogério [UNESP]
Gomes, Marco [UNESP]
Baruchi, Artur [UNESP]
Amaral, Júlio [UNESP]
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Stanzani, Silvio [UNESP]
Cóbe, Raphael [UNESP]
Fialho, Jefferson [UNESP]
Iope, Rogério [UNESP]
Gomes, Marco [UNESP]
Baruchi, Artur [UNESP]
Amaral, Júlio [UNESP]
dc.subject.por.fl_str_mv Heterogeneous systems
Parallel processing
Performance prediction
topic Heterogeneous systems
Parallel processing
Performance prediction
description Performance prediction of applications has always been a great challenge, even for homogeneous architectures. However, today’s trend is the design of cluster running in a heterogeneous architecture, which increases the complexity of new strategies to predict the behavior and time spent by an application to run. In this paper we present a strategy that predicts the performance of an application on different architectures and rank then according to the performance that the application can achieve on each architecture. The proposed strategy was able to correctly rank three of four applications tested without overhead implications. Our next step is to extend the metrics in order to increase the accuracy.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
2022-04-28T19:27:08Z
2022-04-28T19:27:08Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-030-15996-2_18
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11333 LNCS, p. 247-253.
1611-3349
0302-9743
http://hdl.handle.net/11449/221286
10.1007/978-3-030-15996-2_18
2-s2.0-85064598015
url http://dx.doi.org/10.1007/978-3-030-15996-2_18
http://hdl.handle.net/11449/221286
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11333 LNCS, p. 247-253.
1611-3349
0302-9743
10.1007/978-3-030-15996-2_18
2-s2.0-85064598015
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 247-253
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1803047152894607360

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