{"id":959,"date":"2010-10-28T04:06:55","date_gmt":"2010-10-28T04:06:55","guid":{"rendered":"http:\/\/hgpu.org\/?p=959"},"modified":"2010-10-28T04:06:55","modified_gmt":"2010-10-28T04:06:55","slug":"an-implementation-of-the-smooth-particle-mesh-ewald-method-on-gpu-hardware","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=959","title":{"rendered":"An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware"},"content":{"rendered":"<p>The smooth particle mesh Ewald summation method is widely used to efficiently compute long-range electrostatic force terms in molecular dynamics simulations, and there has been considerable work in developing optimized implementations for a variety of parallel computer architectures. We describe an implementation for Nvidia graphical processing units (GPUs) which are general purpose computing devices with a high degree of intrinsic parallelism and arithmetic performance. We find that, for typical biomolecular simulations (e.g., DHFR, 26K atoms), a single GPU equipped workstation is able to provide sufficient performance to permit simulation rates of \u224850 ns\/day when used in conjunction with the ACEMD molecular dynamics package(1) and exhibits an accuracy comparable to that of a reference double-precision CPU implementation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The smooth particle mesh Ewald summation method is widely used to efficiently compute long-range electrostatic force terms in molecular dynamics simulations, and there has been considerable work in developing optimized implementations for a variety of parallel computer architectures. We describe an implementation for Nvidia graphical processing units (GPUs) which are general purpose computing devices with [&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":[66,3],"tags":[1790,165,112,20],"class_list":["post-959","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-paper","tag-chemistry","tag-computational-chemistry","tag-molecular-dynamics","tag-nvidia"],"views":2624,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/959","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=959"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/959\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=959"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=959"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=959"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}