{"id":4366,"date":"2011-06-16T10:00:15","date_gmt":"2011-06-16T10:00:15","guid":{"rendered":"http:\/\/hgpu.org\/?p=4366"},"modified":"2011-06-16T10:00:15","modified_gmt":"2011-06-16T10:00:15","slug":"adapting-mom-with-rwg-basis-functions-to-gpu-technology-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4366","title":{"rendered":"Adapting MoM with RWG Basis Functions to GPU Technology Using CUDA"},"content":{"rendered":"<p>In this paper, a CUDA-enabled GPU accelerated implementation of Method of Moments (MoM) for solving three dimensional conducting body-wire problems is presented. The solution is based on the Mixed Potential Integral Equation (MPIE) discretized using Rao-Wilton-Glisson (RWG) basis functions. The CUDA environment is employed to port a single-CPU sequential code to the parallel GPU platform, and some relevant issues are discussed. Numerical results are given for a helical antenna with a cylindrical cup reflector. The measured speedup of about 8 times over the CPU implementation is demonstrated.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, a CUDA-enabled GPU accelerated implementation of Method of Moments (MoM) for solving three dimensional conducting body-wire problems is presented. The solution is based on the Mixed Potential Integral Equation (MPIE) discretized using Rao-Wilton-Glisson (RWG) basis functions. The CUDA environment is employed to port a single-CPU sequential code to the parallel GPU platform, [&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-4366","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-electrodynamics","category-paper","tag-cuda","tag-electrodynamics","tag-nvidia"],"views":2337,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4366","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=4366"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4366\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4366"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4366"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4366"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}