{"id":9729,"date":"2013-07-02T23:58:42","date_gmt":"2013-07-02T20:58:42","guid":{"rendered":"http:\/\/hgpu.org\/?p=9729"},"modified":"2013-07-02T23:58:42","modified_gmt":"2013-07-02T20:58:42","slug":"gpu-accelerated-fluid-flow-computations-using-the-latice-boltzmann-method","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9729","title":{"rendered":"GPU Accelerated Fluid Flow Computations Using the Latice Boltzmann Method"},"content":{"rendered":"<p>We propose a numerical implementation based on a Graphics Processing Unit (GPU) for the acceleration of the execution time of the Lattice Boltzmann Method. The performance analysis is based on three three-dimensional benchmark applications: Poisseuille flow, lid-driven cavity flow and flow in an elbow shaped domain. Three different, recently released GPU cards are considered for the parallel implementation. To correctly evaluate the speed-up potential of the GPUs, both single-core and multi-core CPU based implementations are used. The results indicate that the GTX 680 GPU card leads to the best performance, with a speed-up ranging between 6.7 and 14.35 over the multi-core CPU based implementation, depending on the application and on the grid density.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a numerical implementation based on a Graphics Processing Unit (GPU) for the acceleration of the execution time of the Lattice Boltzmann Method. The performance analysis is based on three three-dimensional benchmark applications: Poisseuille flow, lid-driven cavity flow and flow in an elbow shaped domain. Three different, recently released GPU cards are considered for [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,104,3],"tags":[14,1795,108,20,1015,1452,1306],"class_list":["post-9729","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-cuda","tag-fluid-dynamics","tag-lattice-boltzmann-model","tag-nvidia","tag-nvidia-geforce-gtx-460","tag-nvidia-geforce-gtx-650","tag-nvidia-geforce-gtx-680"],"views":2614,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9729","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=9729"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9729\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9729"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9729"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}