AUTODOCK is a molecular docking application that consists of a genetic algorithm coupled with the Solis-Wets localsearch method. Despite its wide usage, its power consumption on heterogeneous systems has not been evaluated extensively. In this work, we evaluate the energy efficiency of an OpenCL-accelerated version of AUTODOCK that, along with the traditional SolisWets method, newly incorporates the ADADELTA gradient-based local search. Executions on a Nvidia V100 GPU yielded energy efficiency improvements of up to 297x (Solis-Wets) and 137x (ADADELTA) with respect to the original AUTODOCK baseline.<\/p>\n","protected":false},"excerpt":{"rendered":"
AUTODOCK is a molecular docking application that consists of a genetic algorithm coupled with the Solis-Wets localsearch method. Despite its wide usage, its power consumption on heterogeneous systems has not been evaluated extensively. In this work, we evaluate the energy efficiency of an OpenCL-accelerated version of AUTODOCK that, along with the traditional SolisWets method, newly […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[10,66,90,3],"tags":[2040,7,1781,1790,344,452,1588,20,1793,1963],"class_list":["post-19876","post","type-post","status-publish","format-standard","hentry","category-biology","category-chemistry","category-opencl","category-paper","tag-amd-radeon-rx-vega-56","tag-ati","tag-biology","tag-chemistry","tag-energy-efficient-computing","tag-heterogeneous-systems","tag-molecular-docking","tag-nvidia","tag-opencl","tag-tesla-v100"],"views":2063,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/19876","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=19876"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/19876\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=19876"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=19876"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=19876"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}