{"id":1100,"date":"2010-11-02T15:44:02","date_gmt":"2010-11-02T15:44:02","guid":{"rendered":"http:\/\/hgpu.org\/?p=1100"},"modified":"2010-11-02T15:44:02","modified_gmt":"2010-11-02T15:44:02","slug":"implementation-of-a%c2%a0lattice%e2%80%93boltzmann-method-for-numerical-fluid-mechanics-using-the-nvidia-cuda-technology","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1100","title":{"rendered":"Implementation of a\u00a0Lattice\u2013Boltzmann method for numerical fluid mechanics using the nVIDIA CUDA technology"},"content":{"rendered":"<p>The Lattice-Boltzmann method (LBM) is a distribution-function based approach to numerical fluid mechanics. Due to the simple formulation of the underlying algorithm this method is well suited for parallelization and hardware acceleration using general purpose graphical processing units (GPGPU). Within this work LBM has been implemented in a new code with multi-GPU support and physically validated for a flow around a sphere. The performance analysis shows a remarkable speed-up of 1840% using 3 GPU\u2019s in comparison to a single socket multi core CPU calculation. Moreover the validation for the test case chosen shows excellent agreement with available reference data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Lattice-Boltzmann method (LBM) is a distribution-function based approach to numerical fluid mechanics. Due to the simple formulation of the underlying algorithm this method is well suited for parallelization and hardware acceleration using general purpose graphical processing units (GPGPU). Within this work LBM has been implemented in a new code with multi-GPU support and physically [&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":[14,1795,106,108,356,20,357,119,199,244],"class_list":["post-1100","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-cuda","tag-fluid-dynamics","tag-gpu-cluster","tag-lattice-boltzmann-model","tag-lbultra","tag-nvidia","tag-nvidia-geforce-8800-gts","tag-presentation","tag-tesla-c1060","tag-tesla-s1070"],"views":3580,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1100","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=1100"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1100\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}