{"id":3138,"date":"2011-03-08T21:50:55","date_gmt":"2011-03-08T21:50:55","guid":{"rendered":"http:\/\/hgpu.org\/?p=3138"},"modified":"2011-03-08T21:50:55","modified_gmt":"2011-03-08T21:50:55","slug":"porting-of-an-edge-based-cfd-solver-to-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3138","title":{"rendered":"Porting of an Edge-Based CFD Solver to GPUs"},"content":{"rendered":"<p>Graphics processing units (GPUs) are increasingly becoming a mainstream platform for high performance computational fluid dynamics. This paper describes the porting of a substantial portion of FEFLO, an adaptive, edge-based finite element code for the solution of compressible and incompressible flow, to run on GPUs. The code is primarily written in Fortran 77 and has been ported to vector, shared memory parallel (via OpenMP) and distributed memory parallel (via MPI) machines.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Graphics processing units (GPUs) are increasingly becoming a mainstream platform for high performance computational fluid dynamics. This paper describes the porting of a substantial portion of FEFLO, an adaptive, edge-based finite element code for the solution of compressible and incompressible flow, to run on GPUs. The code is primarily written in Fortran 77 and has [&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":[89,104,3],"tags":[14,212,1795,20,252],"class_list":["post-3138","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-cuda","tag-finite-element-method","tag-fluid-dynamics","tag-nvidia","tag-openmp"],"views":2018,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3138","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=3138"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3138\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3138"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3138"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3138"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}