{"id":2899,"date":"2011-02-19T14:59:39","date_gmt":"2011-02-19T14:59:39","guid":{"rendered":"http:\/\/hgpu.org\/?p=2899"},"modified":"2011-02-19T14:59:39","modified_gmt":"2011-02-19T14:59:39","slug":"using-gpu-vsipl-cuda-to-accelerate-rf-clutter-simulation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2899","title":{"rendered":"Using GPU VSIPL &#038; CUDA to Accelerate RF Clutter Simulation"},"content":{"rendered":"<p>This paper describes a flexible simulator for background Radio Frequency clutter developed at the Georgia Tech Research Institute, and how this simulation was accelerated with the use of nVidia GPUs using GPU VSIPL. The paper describes the mathematical basis for the simulation and how it can be used to simulate RF environments and scenarios; introduces the VSIPL API; describes the porting and validation process; highlights challenges raised by the conversion from double to single precision, and how they were met; and describes the techniques used to obtain improved execution speed, achieving 70x improvement over the original simulation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes a flexible simulator for background Radio Frequency clutter developed at the Georgia Tech Research Institute, and how this simulation was accelerated with the use of nVidia GPUs using GPU VSIPL. The paper describes the mathematical basis for the simulation and how it can be used to simulate RF environments and scenarios; introduces [&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":[11,89,3,41],"tags":[1782,14,625,20,311,1789],"class_list":["post-2899","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-signal-processing","tag-computer-science","tag-cuda","tag-mixed-precision","tag-nvidia","tag-nvidia-geforce-9800-gx2","tag-signal-processing"],"views":2107,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2899","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=2899"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2899\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2899"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2899"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2899"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}