{"id":6407,"date":"2011-11-27T12:28:54","date_gmt":"2011-11-27T10:28:54","guid":{"rendered":"http:\/\/hgpu.org\/?p=6407"},"modified":"2011-11-27T12:28:54","modified_gmt":"2011-11-27T10:28:54","slug":"numerical-precision-and-benchmarking-very-high-order-integration-of-particle-dynamics-on-gpu-accelerators","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6407","title":{"rendered":"Numerical Precision and Benchmarking Very-High-Order Integration of Particle Dynamics on GPU Accelerators"},"content":{"rendered":"<p>GPUs offer a powerful acceleration platform for many scientific applications. Numerical integration of classical Newtonian dynamical particles often requires very high-order numerical accuracy. We assess the floating-point precision and performance of various GPUs for applications involving high-order time-step integration methods for particle model simulations using N-squared interactions. We demonstrate how high-order algorithms can be expressed in Compute Unified Device Architecture (CUDA) and present some detailed benchmark data. We show the high numerical power of high-order integration methods such as Hairer&#8217;s 10th order method and relate its performance to its precision requirements.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GPUs offer a powerful acceleration platform for many scientific applications. Numerical integration of classical Newtonian dynamical particles often requires very high-order numerical accuracy. We assess the floating-point precision and performance of various GPUs for applications involving high-order time-step integration methods for particle model simulations using N-squared interactions. We demonstrate how high-order algorithms can be expressed [&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":[36,11,89,3],"tags":[1787,451,1782,14,258,20,253,436,379,974,1006],"class_list":["post-6407","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-benchmarking","tag-computer-science","tag-cuda","tag-n-body-simulation","tag-nvidia","tag-nvidia-geforce-gtx-260","tag-nvidia-geforce-gtx-295","tag-nvidia-geforce-gtx-480","tag-nvidia-geforce-gtx-580","tag-tesla-c2070"],"views":1952,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6407","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=6407"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6407\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6407"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6407"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6407"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}