{"id":9663,"date":"2013-06-26T23:57:21","date_gmt":"2013-06-26T20:57:21","guid":{"rendered":"http:\/\/hgpu.org\/?p=9663"},"modified":"2013-06-26T23:57:21","modified_gmt":"2013-06-26T20:57:21","slug":"optimization-procedures-during-parallelization-of-specialized-software-for-fluid-flow-simulations","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9663","title":{"rendered":"Optimization procedures during parallelization of specialized software for fluid flow simulations"},"content":{"rendered":"<p>Modern fluid flow simulations can be extremely complex and computationally demanding. Using GPU devices (Graphics Processing Unit) they can execute up to several tens of times faster and simulations can be observed interactively. In this study the basic principles of GPU programming are applied to the implementation of lattice Boltzmann (LB) method. The software that was developed based on the basic LB equations is parallelized and a discussion is given about certain improvements made on the initial implementation.The developed software was tested on a Tesla GPU device and significant speed-up is obtained, when comparing to the traditional version of the software. Fluid flow simulations in the field of biomedicine that needed up to a few hours to be performed, can now be finish in just a few minutes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern fluid flow simulations can be extremely complex and computationally demanding. Using GPU devices (Graphics Processing Unit) they can execute up to several tens of times faster and simulations can be observed interactively. In this study the basic principles of GPU programming are applied to the implementation of lattice Boltzmann (LB) method. The software that [&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":[14,1795,121,108,20,1226],"class_list":["post-9663","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-cuda","tag-fluid-dynamics","tag-fluid-simulation","tag-lattice-boltzmann-model","tag-nvidia","tag-tesla-c2075"],"views":2082,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9663","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=9663"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9663\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9663"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9663"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9663"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}