{"id":7716,"date":"2012-06-06T15:44:25","date_gmt":"2012-06-06T12:44:25","guid":{"rendered":"http:\/\/hgpu.org\/?p=7716"},"modified":"2012-06-06T15:44:25","modified_gmt":"2012-06-06T12:44:25","slug":"classical-mechanical-hard-core-particles-simulated-in-a-rigid-enclosure-using-multi-gpu-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7716","title":{"rendered":"Classical Mechanical Hard-Core Particles Simulated in a Rigid Enclosure using Multi-GPU Systems"},"content":{"rendered":"<p>Hard-core interacting particle methods are of increasing importance for simulations and game applications as well as a tool supporting animations. We develop a high accuracy numerical integration technique for managing hard-core colliding particles of various physical properties such as differing interaction species and hard-core radii using multiple Graphical Processing Unit (m-GPU) computing techniques. We report on the performance tradeoffs between communications and computations for various model parameters and for a range of individual GPU models and multiple-GPU combinations. We explore uses of the GPU Direct communications mechanisms between multiple GPUs accelerating the same CPU host and show that m-GPU multi-level parallelism is a powerful approach for complex N-Body simulations that will deploy well on commodity systems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hard-core interacting particle methods are of increasing importance for simulations and game applications as well as a tool supporting animations. We develop a high accuracy numerical integration technique for managing hard-core colliding particles of various physical properties such as differing interaction species and hard-core radii using multiple Graphical Processing Unit (m-GPU) computing techniques. We report [&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":[11,89,3],"tags":[1782,14,106,258,20,1006],"class_list":["post-7716","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-gpu-cluster","tag-n-body-simulation","tag-nvidia","tag-tesla-c2070"],"views":1993,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7716","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=7716"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7716\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7716"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7716"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7716"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}