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dc.contributor.authorSánchez Barrera, Isaac
dc.contributor.authorCasas, Marc
dc.contributor.authorMoreto Planas, Miquel
dc.contributor.authorAyguadé Parra, Eduard
dc.contributor.authorLabarta Mancho, Jesús José
dc.contributor.authorValero Cortés, Mateo
dc.date.accessioned2016-10-26T12:12:15Z
dc.date.available2016-10-26T12:12:15Z
dc.date.issued2015-05-05
dc.identifier.urihttp://hdl.handle.net/2117/91105
dc.description.abstractCurrent high performance computing architectures are composed of large shared memory NUMA nodes, among other components. Such nodes are becoming increasingly complex as they have several NUMA domains with different access latencies depending on the core where the access is issued. In this work, we propose techniques based on graph partitioning to efficiently mitigate the negative impact of NUMA effects on parallel applications performance, which are able to improve the execution time of OpenMP parallel codes 2.02× times on average when run on architectures with strong NUMA effects.
dc.format.extent3 p.
dc.language.isoeng
dc.publisherBarcelona Supercomputing Center
dc.relation.ispartofBSC International Doctoral Symposium (3rd: 2016: Barcelona)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures paral·leles
dc.subject.lcshHigh performance computing
dc.subject.otherNUMA nodes
dc.subject.otherOpenMP
dc.subject.otherGraph partitioning
dc.titleUsing graph partitioning to accelerate task-based parallel applications
dc.typeConference report
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.rights.accessOpen Access
upcommons.citation.contributor3rd BSC International Doctoral Symposium
upcommons.citation.pubplaceBarcelona
upcommons.citation.publicationNameBook of abstracts
upcommons.citation.startingPage109
upcommons.citation.endingPage111


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain