{"id":919,"date":"2010-10-27T13:09:43","date_gmt":"2010-10-27T13:09:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=919"},"modified":"2010-10-27T13:09:43","modified_gmt":"2010-10-27T13:09:43","slug":"acemd-accelerating-biomolecular-dynamics-in-the-microsecond-time-scale-4","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=919","title":{"rendered":"ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale"},"content":{"rendered":"<p>The high arithmetic performance and intrinsic parallelism of recent graphical processing units (GPUs) can offer a technological edge for molecular dynamics simulations. ACEMD is a production-class biomolecular dynamics (MD) engine supporting CHARMM and AMBER force fields. Designed specifically for GPUs it is able to achieve supercomputing scale performance of 40 ns\/day for all-atom protein systems with over 23\u2009000 atoms. We provide a validation and performance evaluation of the code and run a microsecond-long trajectory for an all-atom molecular system in explicit TIP3P water on a single workstation computer equipped with just 3 GPUs. We believe that microsecond time scale molecular dynamics on cost-effective hardware will have important methodological and scientific implications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The high arithmetic performance and intrinsic parallelism of recent graphical processing units (GPUs) can offer a technological edge for molecular dynamics simulations. ACEMD is a production-class biomolecular dynamics (MD) engine supporting CHARMM and AMBER force fields. Designed specifically for GPUs it is able to achieve supercomputing scale performance of 40 ns\/day for all-atom protein systems [&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":[66,89,3,12],"tags":[109,1790,98,14,106,112,20,234,176,1783,199],"class_list":["post-919","post","type-post","status-publish","format-standard","hentry","category-chemistry","category-nvidia-cuda","category-paper","category-physics","tag-biological-physics","tag-chemistry","tag-computational-physics","tag-cuda","tag-gpu-cluster","tag-molecular-dynamics","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-package","tag-physics","tag-tesla-c1060"],"views":3729,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/919","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=919"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/919\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=919"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=919"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=919"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}