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Analytical Communication Performance Models as a metric in the partitioning of data-parallel kernels on heterogeneous platforms

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dc.contributor.authorRico Gallego, Juan Antonio
dc.contributor.authorDíaz Martín, Juan Carlos
dc.contributor.authorCalvo Jurado, Carmen
dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorGarcía Zapata, Juan Luis
dc.contributor.orcidhttps://orcid.org/0000-0002-4264-7473
dc.contributor.orcidhttps://orcid.org/0000-0002-8435-3844
dc.contributor.orcidhttps://orcid.org/0000-0001-9842-081X
dc.contributor.orcidhttps://orcid.org/0000-0003-1419-1672
dc.date.accessioned2024-11-14T10:53:16Z
dc.date.available2024-11-14T10:53:16Z
dc.date.issued2019
dc.descriptionThe registered version of this article, first published in “The Journal of Supercomputing, 75", is available online at the publisher's website: Springer, https://doi.org/10.1007/s11227-018-2724-8 La versión registrada de este artículo, publicado por primera vez en “The Journal of Supercomputing, 75", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s11227-018-2724-8
dc.description.abstractData partitioning on heterogeneous HPC platforms is formulated as an optimization problem. The algorithm departs from the communication performance models of the processes representing their speeds and outputs a data tiling that minimizes the communication cost. Traditionally, communication volume is the metric used to guide the partitioning, but such metric is unable to capture the complexities introduced by uneven communication channels and the variety of patterns in the kernel communications. We discuss Analytical Communication Performance Models as a new metric in partitioning algorithms. They have not been considered in the past because of two reasons: prediction inaccuracy and lack of tools to automatically build and solve kernel communication formal expressions. We show how communication performance models fit the specific kernel and platform, and we present results that equal or even improve previous volume-based strategies.en
dc.description.versionversión final
dc.identifier.citationJuan A. Rico-Gallego, Juan C. Díaz-Martín, Carmen Calvo-Jurado, Sergio Moreno-Álvarez & Juan L. García-Zapata. "Analytical Communication Performance Models as a metric in the partitioning of data-parallel kernels on heterogeneous platforms". The Journal of Supercomputing, 75, 13 December 2018, 1654–1669
dc.identifier.doihttps://doi.org/10.1007/s11227-018-2724-8
dc.identifier.issn1573-0484
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24376
dc.journal.titleThe Journal of Supercomputing
dc.journal.volume75
dc.language.isoen
dc.page.final1669
dc.page.initial1654
dc.publisherSpringer
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordspartitioning algorithmsen
dc.subject.keywordscommunication performance modelsen
dc.subject.keywordscommunication optimizationen
dc.subject.keywordshybrid data-parallel kernelsen
dc.titleAnalytical Communication Performance Models as a metric in the partitioning of data-parallel kernels on heterogeneous platformsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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