Vortex particle methods, when combined with multipole-accelerated boundary element methods (BEM), become a complete tool for direct numerical simulation (DNS) of internal or external vortex-dominated flows. In previous work, we presented a method to accelerate the vorticity-velocity inversion at the heart of vortex particle methods by performing a multipole treecode N-body method on parallel graphics hardware. The resulting method achieved a 17-fold speedup over a dual-core CPU implementation. In the present work, we will demonstrate both an improved algorithm for the GPU vortex particle method that outperforms an 8-core CPU by a factor of 43, but also a GPU-accelerated multipole treecode method for the boundary element solution. The new BEM solves for the unknown source, dipole, or combined strengths over a triangulated surface using all available CPU cores and GPUs. Problems with up to 1.4 million unknowns can be solved on a single commodity desktop computer in one minute, and at that size the hybrid CPU\/GPU outperforms a quad-core CPU alone by 22.5 times. The method is exercised on DNS of impulsively-started flow over spheres at Re=500, 1000, 2000, and 4000.<\/p>\n","protected":false},"excerpt":{"rendered":"
Vortex particle methods, when combined with multipole-accelerated boundary element methods (BEM), become a complete tool for direct numerical simulation (DNS) of internal or external vortex-dominated flows. In previous work, we presented a method to accelerate the vorticity-velocity inversion at the heart of vortex particle methods by performing a multipole treecode N-body method on parallel graphics […]<\/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":[104,3],"tags":[1795,121,106,20,373,244],"class_list":["post-4236","post","type-post","status-publish","format-standard","hentry","category-fluid-dynamics","category-paper","tag-fluid-dynamics","tag-fluid-simulation","tag-gpu-cluster","tag-nvidia","tag-nvidia-geforce-gtx-275","tag-tesla-s1070"],"views":2030,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4236","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=4236"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4236\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4236"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4236"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4236"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}