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dc.contributor.authorTeijeiro, Carlos
dc.contributor.authorLópez Taboada, Guillermo
dc.contributor.authorTouriño, Juan
dc.contributor.authorDoallo Biempica, Ramón
dc.date.accessioned2019-04-16T14:20:58Z
dc.date.available2019-04-16T14:20:58Z
dc.date.issued2012-01-03
dc.identifier.citationTEIJEIRO, Carlos, et al. Design and Implementation of MapReduce using the PGAS Programming Model with UPC. En 2011 IEEE 17th International Conference on Parallel and Distributed Systems. IEEE, 2011. p. 196-203.es_ES
dc.identifier.issn1521-9097
dc.identifier.urihttp://hdl.handle.net/2183/22669
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in International Conference on Parallel and Distributed Systems. Proceedings. The final authenticated version is available online at: http://dx.doi.org/10.1109/ICPADS.2011.162es_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.sponsorshipMinisterio de Ciencia e Innovación; TIN2010-16735es_ES
dc.language.isoenges_ES
dc.publisherIEEE Computer Societyes_ES
dc.relation.urihttp://dx.doi.org/10.1109/ICPADS.2011.162es_ES
dc.subjectUPCes_ES
dc.subjectMapReducees_ES
dc.subjectHPCes_ES
dc.subjectProgrammabilityes_ES
dc.subjectCollective primitiveses_ES
dc.titleDesign and Implementation of MapReduce using the PGAS Programming Model with UPCes_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleInternational Conference on Parallel and Distributed Systems. Proceedingses_ES
UDC.conferenceTitle2011 IEEE 17th International Conference on Parallel and Distributed Systemses_ES


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