Towards a Strategy for Performance Prediction on Heterogeneous Architectures
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , |
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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Repositório Institucional da UNESP |
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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:29462024-08-05T21:30:42.032459Repositório 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_ |
1808129328853024768 |