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dc.contributor.authorValero-Lara, P.
dc.contributor.authorNookala, P.
dc.contributor.authorPelayo, F.L.
dc.contributor.authorJansson, J.
dc.contributor.authorDimitropoulos, S.
dc.contributor.authorRaicu, I.
dc.date.accessioned2016-06-13T13:10:32Z
dc.date.available2016-06-13T13:10:32Z
dc.date.issued2016-01-01
dc.identifier.issn1895-1767
dc.identifier.urihttp://hdl.handle.net/20.500.11824/69
dc.description.abstractMany-Task Computing (MTC) is a common scenario for multiple parallel systems, such as cluster, grids, cloud and supercomputers, but it is not so popular in shared memory parallel processors. In this sense and given the spectacular growth in performance and in number of cores integrated in many-core architectures, the study of MTC on such architectures is becoming more and more relevant. In this paper, authors present what are those programming mechanisms to take advantages of such massively parallel features for the particular target of MTC. Also, the hardware features of the two dominant many-core platforms (NVIDIA's GPUs and Intel Xeon Phi) are also analyzed for our specific framework. Given the important differences in terms of hardware and software in our two many-core platforms, we have considered different strategies based on CUDA (for GPUs) and OpenMP (for Intel Xeon Phi). We carried out several test cases based on an appropriate and widely studied problem for benchmarking as matrix multiplication. Essentially, this study consisted of comparing the time consumed for computing in parallel several tasks one by one (the whole computational resources are used just to compute one task at a time) with the time consumed for computing in parallel the same set of tasks simultaneously (the whole computational resources are used for computing the set of tasks at very same time). Finally, we compared both software-hardware scenarios to identify the most relevant computer features in each of our many-core architectures.
dc.formatapplication/pdf
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectCore
dc.subjectCuda
dc.subjectGPU
dc.subjectIntel Xeon Phi
dc.subjectMany
dc.subjectMulti-task computing
dc.subjectOpenMP
dc.subjectParallel computing
dc.titleEnhanced variational image dehazing
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doi10.12694/scpe.v17i1.1148
dc.relation.publisherversionhttp://www.scpe.org/index.php/scpe/article/view/1148
dc.relation.projectIDES/1PE/MTM2013-40824-Pen_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersionen_US
dc.journal.titleScalable Computingen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España