{"id":11410,"date":"2014-02-16T00:11:45","date_gmt":"2014-02-15T22:11:45","guid":{"rendered":"http:\/\/hgpu.org\/?p=11410"},"modified":"2014-02-16T00:11:45","modified_gmt":"2014-02-15T22:11:45","slug":"application-of-the-characteristic-basis-function-method-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11410","title":{"rendered":"Application of the Characteristic Basis Function Method using CUDA"},"content":{"rendered":"<p>The Characteristic Basis Function Method (CBFM) is a popular technique for efficiently solving the Method of Moments (MoM) matrix equations. In this work, we address the adaptation of this method to a relatively new computing infrastructure provided by NVIDIA, the Compute Unified Device Architecture (CUDA), and take into account some of the limitations which appear when the geometry under analysis becomes too big to fit into the Graphics Processing Unit&#8217;s (GPU&#8217;s) memory.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Characteristic Basis Function Method (CBFM) is a popular technique for efficiently solving the Method of Moments (MoM) matrix equations. In this work, we address the adaptation of this method to a relatively new computing infrastructure provided by NVIDIA, the Compute Unified Device Architecture (CUDA), and take into account some of the limitations which appear [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,569,20,1226],"class_list":["post-11410","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-cula","tag-nvidia","tag-tesla-c2075"],"views":2062,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11410","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=11410"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11410\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11410"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11410"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11410"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}