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Towards a Strategy for Performance Prediction on Heterogeneous Architectures

dc.contributor.authorStanzani, Silvio [UNESP]
dc.contributor.authorCóbe, Raphael [UNESP]
dc.contributor.authorFialho, Jefferson [UNESP]
dc.contributor.authorIope, Rogério [UNESP]
dc.contributor.authorGomes, Marco [UNESP]
dc.contributor.authorBaruchi, Artur [UNESP]
dc.contributor.authorAmaral, Júlio [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2022-04-28T19:27:08Z
dc.date.available2022-04-28T19:27:08Z
dc.date.issued2019-01-01
dc.description.abstractPerformance 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.en
dc.description.affiliationNúcleo de Computação Científica (NCC) Universidade Estadual Paulista
dc.description.affiliationUnespNúcleo de Computação Científica (NCC) Universidade Estadual Paulista
dc.format.extent247-253
dc.identifierhttp://dx.doi.org/10.1007/978-3-030-15996-2_18
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11333 LNCS, p. 247-253.
dc.identifier.doi10.1007/978-3-030-15996-2_18
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-85064598015
dc.identifier.urihttp://hdl.handle.net/11449/221286
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceScopus
dc.subjectHeterogeneous systems
dc.subjectParallel processing
dc.subjectPerformance prediction
dc.titleTowards a Strategy for Performance Prediction on Heterogeneous Architecturesen
dc.typeTrabalho apresentado em evento

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