{"id":3592,"date":"2011-04-14T20:45:57","date_gmt":"2011-04-14T20:45:57","guid":{"rendered":"http:\/\/hgpu.org\/?p=3592"},"modified":"2011-04-14T20:45:57","modified_gmt":"2011-04-14T20:45:57","slug":"gpu-acceleration-of-method-of-moments-matrix-assembly-using-rao-wilton-glisson-basis-functions","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3592","title":{"rendered":"GPU acceleration of method of moments matrix assembly using Rao-Wilton-Glisson basis functions"},"content":{"rendered":"<p>In this paper, a GPU accelerated implementation of the matrix assembly phase of the methods of moments is presented. The modelling of PEC structures using the electric field integral equation and the Rao-Wilton-Glisson basis functions introduced in is considered. NVIDIA CUDA is used to do the GPU development and the double precision support offered by the GT200 architecture is used to provide a high level of accuracy. The CUDA implementation of the matrix assembly phase considered is able to attain a speedup of 65 times over the CPU implementation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, a GPU accelerated implementation of the matrix assembly phase of the methods of moments is presented. The modelling of PEC structures using the electric field integral equation and the Rao-Wilton-Glisson basis functions introduced in is considered. NVIDIA CUDA is used to do the GPU development and the double precision support offered by [&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":[89,319,3],"tags":[14,1802,20],"class_list":["post-3592","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-electrodynamics","category-paper","tag-cuda","tag-electrodynamics","tag-nvidia"],"views":2030,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3592","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=3592"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3592\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3592"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3592"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3592"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}