{"id":5351,"date":"2011-09-01T13:14:41","date_gmt":"2011-09-01T10:14:41","guid":{"rendered":"http:\/\/hgpu.org\/?p=5351"},"modified":"2011-09-01T13:14:41","modified_gmt":"2011-09-01T10:14:41","slug":"acceleration-of-tm-cylinder-efie-with-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5351","title":{"rendered":"Acceleration of TM cylinder EFIE with CUDA"},"content":{"rendered":"<p>In this paper, we have shown that exploitation of the GPU&#8217;s massively parallel architecture can dramatically increase the speed of MoM calculations. While the code can certainly be improved, matrix fill speed-up factors are already commonly found to be between 150X-260X. The conjugate gradient solver stands to be improved at this writing but still results in performance increases of 1.25X-20X. Continued development of MoM codes with GPU acceleration will undoubtedly bring about a new era of computational electromagnetics.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we have shown that exploitation of the GPU&#8217;s massively parallel architecture can dramatically increase the speed of MoM calculations. While the code can certainly be improved, matrix fill speed-up factors are already commonly found to be between 150X-260X. The conjugate gradient solver stands to be improved at this writing but still results [&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":[89,319,3,12],"tags":[580,14,1802,853,20,226,661,1149,1783],"class_list":["post-5351","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-electrodynamics","category-paper","category-physics","tag-conjugate-gradient-solver","tag-cuda","tag-electrodynamics","tag-field-equations","tag-nvidia","tag-nvidia-geforce-8800-gt","tag-nvidia-quadro-fx-770-m","tag-nvidia-quadro-nvs-140-m","tag-physics"],"views":2308,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5351","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=5351"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5351\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5351"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5351"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5351"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}