{"id":6234,"date":"2011-11-10T17:14:31","date_gmt":"2011-11-10T15:14:31","guid":{"rendered":"http:\/\/hgpu.org\/?p=6234"},"modified":"2011-11-10T17:14:31","modified_gmt":"2011-11-10T15:14:31","slug":"gpu-cuda-performance-on-two-dimensional-and-three-dimensional-vawt-vortex-models","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6234","title":{"rendered":"GPU Cuda Performance on Two-Dimensional and Three-Dimensional VAWT Vortex Models"},"content":{"rendered":"<p>An analytical model of a vertical axis wind turbine was implemented using both a 2-D and a 3-D vortex model. The model requires significant amounts of computational resources and time compelling the use of an improved method for executing the algorithm in a highly parallelized fashion. Graphics Processing Units (GPUs), which are a new highly cost effective method of implementing parallel processing, were used along with a interface code to allow the numerically intensive routine to execute as parallel code on a graphics card. This achieved a substantially reduced computation time compared to a traditional desktop computer model. Using two quad-core processors and a graphics card, a 19.7 fold reduction in computation time was achieved. This paper will describe the model used, the parallel code created, the GPU interface, and an illustration of the performance increases.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>An analytical model of a vertical axis wind turbine was implemented using both a 2-D and a 3-D vortex model. The model requires significant amounts of computational resources and time compelling the use of an improved method for executing the algorithm in a highly parallelized fashion. Graphics Processing Units (GPUs), which are a new highly [&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":[36,89,104,3],"tags":[1787,153,14,1795,20,251],"class_list":["post-6234","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-algorithms","tag-analytical-model","tag-cuda","tag-fluid-dynamics","tag-nvidia","tag-nvidia-geforce-gtx-285"],"views":2009,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6234","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=6234"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6234\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6234"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6234"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6234"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}