With the fast development during the past few years, multicore has become a revolutionary technique for the performance improvement of computing devices, ranging from supercomputers to cell phones. Among multicore processors, a graphics processing units (GPU) is outstanding because of its huge computational performance and comparably low cost. It can be used as a coprocessor of a multicore CPU in mainstream workstations to accelerate various scientific problems. MATLAB is popular for scientific research. Recently, MATLAB parallel computing toolbox incorporates GPU support on many built-in functions, which facilities the usage of GPUs in scientific computing. In this paper, we introduce an effortless technique using a GPU and MATLAB parallel computing toolbox to accelerate the fringe analysis algorithms. Furthermore, we evaluate the performances of multicore CPU with MATLAB parallel computing toolbox, GPU with MATLAB parallel computing toolbox, and GPU with the direct programming in visual studio and computer unified device architecture (CUDA). Their performances are compared based on the examples of Fourier transform and windowed Fourier transform.<\/p>\n","protected":false},"excerpt":{"rendered":"
With the fast development during the past few years, multicore has become a revolutionary technique for the performance improvement of computing devices, ranging from supercomputers to cell phones. Among multicore processors, a graphics processing units (GPU) is outstanding because of its huge computational performance and comparably low cost. It can be used as a coprocessor […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,207,20,251],"class_list":["post-8498","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-fft","tag-nvidia","tag-nvidia-geforce-gtx-285"],"views":2356,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8498","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8498"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8498\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8498"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8498"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8498"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}