{"id":12379,"date":"2014-06-26T09:25:34","date_gmt":"2014-06-26T06:25:34","guid":{"rendered":"http:\/\/hgpu.org\/?p=12379"},"modified":"2014-06-26T09:25:34","modified_gmt":"2014-06-26T06:25:34","slug":"multi-gpu-implementation-of-a-hybrid-thermal-lattice-boltzmann-solver-using-the-thelma-framework","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12379","title":{"rendered":"Multi-GPU Implementation of a Hybrid Thermal Lattice Boltzmann Solver using the TheLMA Framework"},"content":{"rendered":"<p>In this contribution, a single-node multi-GPU thermal lattice Boltzmann solver is presented. We implement a simplified version of the hybrid model developed by Lallemand and Luo in 2003, which combines multiple-relaxation-time lattice Boltzmann for the fluid flow with a finite-difference method for temperature. The program is based on the TheLMA framework which was developed for that purpose. The chosen implementation and optimisation strategies are described, both for inter-GPU communication and for coupling with the thermal component of the model. Validation and performance results are provided as well.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this contribution, a single-node multi-GPU thermal lattice Boltzmann solver is presented. We implement a simplified version of the hybrid model developed by Lallemand and Luo in 2003, which combines multiple-relaxation-time lattice Boltzmann for the fluid flow with a finite-difference method for temperature. The program is based on the TheLMA framework which was developed 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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[89,104,3],"tags":[14,1795,108,20,199],"class_list":["post-12379","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-tesla-c1060"],"views":2130,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12379","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=12379"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12379\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12379"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12379"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}