{"id":1958,"date":"2010-12-11T16:20:39","date_gmt":"2010-12-11T16:20:39","guid":{"rendered":"http:\/\/hgpu.org\/?p=1958"},"modified":"2010-12-11T16:20:39","modified_gmt":"2010-12-11T16:20:39","slug":"particle-in-cell-simulations-with-charge-conserving-current-deposition-on-graphic-processing-units","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1958","title":{"rendered":"Particle-in-cell Simulations with Charge-Conserving Current Deposition on Graphic Processing Units"},"content":{"rendered":"<p>We present an implementation of a 2D fully relativistic, electromagnetic Particle-in-Cell code, with charge-conserving current deposition, on parallel graphics processors (GPU) with CUDA. The GPU implementation achieved a one particle-step process time of 2.52 ns for cold plasma runs and 9.15 ns for extremely relativistic plasma runs, which are respectively 81 and 27 times faster than a single threaded state-of-art CPU code. A particle-based computation thread assignment was used in the current deposition scheme and write conflicts among the threads were resolved by a thread racing technique. A parallel particle sorting scheme was also developed and used. The implementation took advantage of fast on-chip shared memory, and can in principle be extended to 3D.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present an implementation of a 2D fully relativistic, electromagnetic Particle-in-Cell code, with charge-conserving current deposition, on parallel graphics processors (GPU) with CUDA. The GPU implementation achieved a one particle-step process time of 2.52 ns for cold plasma runs and 9.15 ns for extremely relativistic plasma runs, which are respectively 81 and 27 times faster [&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":[89,3,12],"tags":[14,20,234,299,1783,300],"class_list":["post-1958","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-particle-in-cell-methods","tag-physics","tag-plasma-physics"],"views":2195,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1958","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=1958"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1958\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1958"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1958"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1958"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}