{"id":4404,"date":"2011-06-20T10:19:28","date_gmt":"2011-06-20T10:19:28","guid":{"rendered":"http:\/\/hgpu.org\/?p=4404"},"modified":"2011-06-20T10:19:28","modified_gmt":"2011-06-20T10:19:28","slug":"accelerating-batched-1d-fft-with-a-cuda-capable-computer","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4404","title":{"rendered":"Accelerating batched 1D-FFT with a CUDA-capable computer"},"content":{"rendered":"<p>This work concerns the application of CUDA-based software (Compute Unified Device Architecture), developed by NVIDIA for programmable Graphics Processing units (GPUs). CUDA code is written in &#8216;C for CUDA&#8217;, indicating the standard C programming language with NVIDIA extensions.Our goal was to find out, whether batched (multiple) one-dimensional Fast Fourier Transformation (1DFFT), often encountered in various fields of signal processing, can be speeded up significantly by exploiting the parellel-processing power of a low-cost, standard, CUDA-enabled graphics card in a home-assembled PC.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This work concerns the application of CUDA-based software (Compute Unified Device Architecture), developed by NVIDIA for programmable Graphics Processing units (GPUs). CUDA code is written in &#8216;C for CUDA&#8217;, indicating the standard C programming language with NVIDIA extensions.Our goal was to find out, whether batched (multiple) one-dimensional Fast Fourier Transformation (1DFFT), often encountered in various [&hellip;]<\/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":[89,33,3,41],"tags":[14,207,1786,20,466,1789],"class_list":["post-4404","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-image-processing","category-paper","category-signal-processing","tag-cuda","tag-fft","tag-image-processing","tag-nvidia","tag-nvidia-geforce-9600-gt","tag-signal-processing"],"views":2076,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4404","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=4404"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4404\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}