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dc.contributor.advisorUjaldon-Martinez, Manuel 
dc.contributor.authorCabero Guerra, Javier
dc.contributor.otherArquitectura de Computadoreses_ES
dc.date.accessioned2016-05-19T12:18:04Z
dc.date.available2016-05-19T12:18:04Z
dc.date.created2015-09
dc.date.issued2016-05-19
dc.identifier.urihttp://hdl.handle.net/10630/11434
dc.description.abstractAs time has passed, the general purpose programming paradigm has evolved, producing different hardware architectures whose characteristics differ widely. In this work, we are going to demonstrate, through different applications belonging to the field of Image Processing, the existing difference between three Nvidia hardware platforms: two of them belong to the GeForce graphics cards series, the GTX 480 and the GTX 980 and one of the low consumption platforms which purpose is to allow the execution of embedded applications as well as providing an extreme efficiency: the Jetson TK1. With respect to the test applications we will use five examples from Nvidia CUDA Samples. These applications are directly related to Image Processing, as the algorithms they use are similar to those from the field of medical image registration. After the tests, it will be proven that GTX 980 is both the device with the highest computational power and the one that has greater consumption, it will be seen that Jetson TK1 is the most efficient platform, it will be shown that GTX 480 produces more heat than the others and we will learn other effects produced by the existing difference between the architecture of the devices.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectDiagnóstico por imagenes_ES
dc.subjectIngeniería biomédicaes_ES
dc.subjectComputación de altas prestacioneses_ES
dc.subjectGrado en Ingeniería Informática - Trabajos Fin de Grado
dc.subjectInformática - Trabajos Fin de Grado
dc.subject.otherIngeniería Informáticaes_ES
dc.subject.otherCUDAes_ES
dc.subject.otherProcesamiento Imágenes Cerebraleses_ES
dc.titleProcesamiento de imágenes cerebrales en GPUes_ES
dc.title.alternativeNeuroimage processing on GPU using CUDAes_ES
dc.typeinfo:eu-repo/semantics/bachelorThesises_ES
dc.cclicenseby-nc-ndes_ES


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