{"id":6385,"date":"2011-11-25T18:23:06","date_gmt":"2011-11-25T16:23:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=6385"},"modified":"2011-11-25T18:23:06","modified_gmt":"2011-11-25T16:23:06","slug":"high-rayleigh-number-mantle-convection-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6385","title":{"rendered":"High Rayleigh Number Mantle Convection on GPU"},"content":{"rendered":"<p>We implemented two- and three-dimensional Rayleigh-Benard convection on Nvidia GPUs by utilizing a 2nd-order finite difference method. By exploiting the massive parallelism of GPU using both CUDA for C and optimized CUBLAS routines, we have on a single Fermi GPU run simultaneous of Raileigh number up to 6&#215;10^10 (on a mesh of 2000&#215;4000 uniform grid points) in two dimensions and up to 10^7 (on a mesh of 450x450x225 uniform grid points) for three dimensions. On Nvidia Tesla C2070 GPUs, these implementations enjoy single-precision performance of 535 GFLOP\/s and 100 GFLOP\/s respectively, and double-precision performance of 230 GFLOP\/s and 70 GFLOP\/s respectively.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We implemented two- and three-dimensional Rayleigh-Benard convection on Nvidia GPUs by utilizing a 2nd-order finite difference method. By exploiting the massive parallelism of GPU using both CUDA for C and optimized CUBLAS routines, we have on a single Fermi GPU run simultaneous of Raileigh number up to 6&#215;10^10 (on a mesh of 2000&#215;4000 uniform grid [&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":[89,303,3],"tags":[238,14,1801,327,20,1006],"class_list":["post-6385","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-earth-and-space-sciences","category-paper","tag-cublas","tag-cuda","tag-earth-and-space-sciences","tag-finite-difference","tag-nvidia","tag-tesla-c2070"],"views":1887,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6385","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=6385"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6385\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6385"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6385"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6385"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}