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dc.contributor.authorGonzález-Domínguez, Jorge
dc.contributor.authorLópez Taboada, Guillermo
dc.contributor.authorFraguela, Basilio B.
dc.contributor.authorMartín Santamaría, María José
dc.contributor.authorTouriño, Juan
dc.date.accessioned2019-04-16T14:52:53Z
dc.date.available2019-04-16T14:52:53Z
dc.date.issued2010-05-24
dc.identifier.citationGONZÁLEZ-DOMÍNGUEZ, Jorge, et al. Servet: A benchmark suite for autotuning on multicore clusters. En 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS). IEEE, 2010. p. 1-9.es_ES
dc.identifier.urihttp://hdl.handle.net/2183/22670
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS). Proceedings. The final authenticated version is available online at: http://dx.doi.org/10.1109/IPDPS.2010.5470358.es_ES
dc.description.abstract[Abstract] MapReduce is a powerful tool for processing large data sets used by many applications running in distributed environments. However, despite the increasing number of computationally intensive problems that require low-latency communications, the adoption of MapReduce in High Performance Computing (HPC) is still emerging. Here languages based on the Partitioned Global Address Space (PGAS) programming model have shown to be a good choice for implementing parallel applications, in order to take advantage of the increasing number of cores per node and the programmability benefits achieved by their global memory view, such as the transparent access to remote data. This paper presents the first PGAS-based MapReduce implementation that uses the Unified Parallel C (UPC) language, which (1) obtains programmability benefits in parallel programming, (2) offers advanced configuration options to define a customized load distribution for different codes, and (3) overcomes performance penalties and bottlenecks that have traditionally prevented the deployment of MapReduce applications in HPC. The performance evaluation of representative applications on shared and distributed memory environments assesses the scalability of the presented MapReduce framework, confirming its suitability.es_ES
dc.description.sponsorshipXunta de Galicia; INCITE08PXIB105161PRes_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación; TIN2007-67537-C03-02es_ES
dc.description.sponsorshipMinisterio de Educación; FPU; AP2008-01578es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineerses_ES
dc.relation.urihttp://dx.doi.org/10.1109/IPDPS.2010.5470358es_ES
dc.subjectMulticore processinges_ES
dc.subjectHardwarees_ES
dc.subjectComputer architecturees_ES
dc.subjectDelayes_ES
dc.subjectParameter estimationes_ES
dc.subjectLibrarieses_ES
dc.subjectBandwidthes_ES
dc.subjectClusteringes_ES
dc.subjectAlgorithmses_ES
dc.subjectParallel processinges_ES
dc.subjectParallel architectureses_ES
dc.titleServet: A Benchmark Suite for Autotuning on Multicore Clusterses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.conferenceTitle2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS)es_ES


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