{"id":11360,"date":"2014-02-08T02:44:53","date_gmt":"2014-02-08T00:44:53","guid":{"rendered":"http:\/\/hgpu.org\/?p=11360"},"modified":"2014-02-08T02:44:53","modified_gmt":"2014-02-08T00:44:53","slug":"simulating-and-benchmarking-the-shallow-water-fluid-dynamical-equations-on-multiple-graphical-processing-units","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11360","title":{"rendered":"Simulating and Benchmarking the Shallow-Water Fluid Dynamical Equations on Multiple Graphical Processing Units"},"content":{"rendered":"<p>The shallow-water model equations provide a simple yet realistic benchmark problem in computational fluid dynamics (CFD) that can be implemented on a variety of computational platforms. Graphical Processing Units can be used to accelerate such problems either singly using a data parallel decompositional scheme or with multiple devices using a domain decompositional approach. We implement the SW equations on a range of modern GPUs with both parallel schemes and report on the typical performance. We compare integer optimised GPUs and very modern floating-point intensive GPU devices such as NVidia&#8217;s Kepler K20X, and also investigate different m-GPU communication methods for geometric decompositions. We give detailed performance results and a summary of the main parallelisation issues.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The shallow-water model equations provide a simple yet realistic benchmark problem in computational fluid dynamics (CFD) that can be implemented on a variety of computational platforms. Graphical Processing Units can be used to accelerate such problems either singly using a data parallel decompositional scheme or with multiple devices using a domain decompositional approach. We implement [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,104,3],"tags":[451,14,1795,20,1390],"class_list":["post-11360","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-benchmarking","tag-cuda","tag-fluid-dynamics","tag-nvidia","tag-tesla-k20"],"views":1948,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11360","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=11360"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11360\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11360"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11360"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11360"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}