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dc.contributor.authorExpósito, Roberto R.
dc.contributor.authorLópez Taboada, Guillermo
dc.contributor.authorRamos Garea, Sabela
dc.contributor.authorTouriño, Juan
dc.contributor.authorDoallo Biempica, Ramón
dc.date.accessioned2018-07-06T15:24:15Z
dc.date.available2018-07-06T15:24:15Z
dc.date.issued2013-08
dc.identifier.citationExpósito, R. R., Taboada, G. L., Ramos, S., Touriño, J., & Doallo, R. (2013). General‐purpose computation on GPUs for high performance cloud computing. Concurrency and Computation: Practice and Experience, 25(12), 1628-1642.es_ES
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.urihttp://hdl.handle.net/2183/20871
dc.descriptionThis is the peer reviewed version of the following article: Expósito, R. R., Taboada, G. L., Ramos, S., Touriño, J., & Doallo, R. (2013). General‐purpose computation on GPUs for high performance cloud computing. Concurrency and Computation: Practice and Experience, 25(12), 1628-1642., which has been published in final form at https://doi.org/10.1002/cpe.2845. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.es_ES
dc.description.abstract[Abstract] Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynamically scalable resources as a service over the Internet. In addition, General‐Purpose computation on Graphical Processing Units (GPGPU) has gained much attention from scientific computing in multiple domains, thus becoming an important programming model in HPC. Compute Unified Device Architecture (CUDA) has been established as a popular programming model for GPGPUs, removing the need for using the graphics APIs for computing applications. Open Computing Language (OpenCL) is an emerging alternative not only for GPGPU but also for any parallel architecture. GPU clusters, usually programmed with a hybrid parallel paradigm mixing Message Passing Interface (MPI) with CUDA/OpenCL, are currently gaining high popularity. Therefore, cloud providers are deploying clusters with multiple GPUs per node and high‐speed network interconnects in order to make them a feasible option for HPC as a Service (HPCaaS). This paper evaluates GPGPU for high performance cloud computing on a public cloud computing infrastructure, Amazon EC2 Cluster GPU Instances (CGI), equipped with NVIDIA Tesla GPUs and a 10 Gigabit Ethernet network. The analysis of the results, obtained using up to 64 GPUs and 256‐processor cores, has shown that GPGPU is a viable option for high performance cloud computing despite the significant impact that virtualized environments still have on network overhead, which still hampers the adoption of GPGPU communication‐intensive applications. Copyrightes_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación; TIN2010-16735es_ES
dc.language.isoenges_ES
dc.publisherJohn Wiley & Sons Ltd.es_ES
dc.relation.urihttps://doi.org/10.1002/cpe.2845es_ES
dc.subjectCloud Computinges_ES
dc.subjectGeneral‐Purporse computation on GPU (GPGPU)es_ES
dc.subjectHigh performance computing (HPC)es_ES
dc.subject10 Gigabit ethernetes_ES
dc.subjectCUDAes_ES
dc.subjectOpenCLes_ES
dc.subjectMPIes_ES
dc.titleGeneral‐purpose computation on GPUs for high performance cloud computinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleConcurrency and Computation: Practice & Experiencees_ES
UDC.volume25es_ES
UDC.issue12es_ES
UDC.startPage1628es_ES
UDC.endPage1642es_ES
dc.identifier.doi10.1002/cpe.2845


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