{"id":5109,"date":"2011-08-15T16:08:03","date_gmt":"2011-08-15T13:08:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=5109"},"modified":"2011-08-18T21:25:02","modified_gmt":"2011-08-18T18:25:02","slug":"fpga-acceleration-of-rigid-molecule-docking-codes","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5109","title":{"rendered":"FPGA acceleration of rigid-molecule docking codes"},"content":{"rendered":"<p>Modelling the interactions of biological molecules, or docking, is critical both to understanding basic life processes and to designing new drugs. The field programmable gate array (FPGA) based acceleration of a recently developed, complex, production docking code is described. The authors found that it is necessary to extend their previous three-dimensional (3D) correlation structure in several ways, most significantly to support simultaneous computation of several correlation functions. The result for small-molecule docking is a 100-fold speed-up of a section of the code that represents over 95% of the original run-time. An additional 2% is accelerated through a previously described method, yielding a total acceleration of 36x over a single core and 10x over a quad-core. This approach is found to be an ideal complement to graphics processing unit (GPU) based docking, which excels in the protein-protein domain.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modelling the interactions of biological molecules, or docking, is critical both to understanding basic life processes and to designing new drugs. The field programmable gate array (FPGA) based acceleration of a recently developed, complex, production docking code is described. The authors found that it is necessary to extend their previous three-dimensional (3D) correlation structure in [&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":[10,89,3],"tags":[1781,658,14,377,264,20,67,199],"class_list":["post-5109","post","type-post","status-publish","format-standard","hentry","category-biology","category-nvidia-cuda","category-paper","tag-biology","tag-biomolecules","tag-cuda","tag-fpga","tag-molecular-modeling","tag-nvidia","tag-performance","tag-tesla-c1060"],"views":2100,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5109","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=5109"}],"version-history":[{"count":1,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5109\/revisions"}],"predecessor-version":[{"id":5202,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5109\/revisions\/5202"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5109"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5109"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5109"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}