{"id":2301,"date":"2011-01-03T21:25:13","date_gmt":"2011-01-03T21:25:13","guid":{"rendered":"http:\/\/hgpu.org\/?p=2301"},"modified":"2011-01-03T21:25:13","modified_gmt":"2011-01-03T21:25:13","slug":"fpga-based-acceleration-of-charmm-potential-minimization","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2301","title":{"rendered":"FPGA-based acceleration of CHARMM-potential minimization"},"content":{"rendered":"<p>Energy minimization is an important step in molecular modeling, with applications in molecular docking and in mapping binding sites. Minimization involves repeated evaluation of various bonded and non-bonded energies of a protein complex. It is a computationally expensive process, with runtimes typically being many hours on a desktop system. In the current article, we present acceleration of the energy evaluation phase of minimization using Field Programmable Gate Arrays. We project a multiple orders-of-magnitude speed-up over a single CPU core and a factor of 8 speed-up over our previous acceleration using an NVIDIA Tesla 1060 GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Energy minimization is an important step in molecular modeling, with applications in molecular docking and in mapping binding sites. Minimization involves repeated evaluation of various bonded and non-bonded energies of a protein complex. It is a computationally expensive process, with runtimes typically being many hours on a desktop system. In the current article, we present [&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":[1790,377,264,20,67,199],"class_list":["post-2301","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-paper","tag-chemistry","tag-fpga","tag-molecular-modeling","tag-nvidia","tag-performance","tag-tesla-c1060"],"views":1999,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2301","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=2301"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2301\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2301"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2301"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}