{"id":2581,"date":"2011-01-23T14:53:18","date_gmt":"2011-01-23T14:53:18","guid":{"rendered":"http:\/\/hgpu.org\/?p=2581"},"modified":"2011-01-23T14:53:18","modified_gmt":"2011-01-23T14:53:18","slug":"a-study-of-parallel-evolution-strategy-pattern-search-on-a-gpu-computing-platform","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2581","title":{"rendered":"A study of parallel evolution strategy: pattern search on a GPU computing platform"},"content":{"rendered":"<p>This paper presents a massively parallel Evolution Strategy &#8211; Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization functions. The algorithm is specifically designed for a graphic processing unit (GPU) hardware platform featuring &#8216;Single Instruction &#8211; Multiple Thread&#8217; (SIMT). GPU computing is an emerging desktop parallel computing platform. The hybrid ES-PS optimization method is implemented in the GPU environment and compared to a similar implementation on CPU hardware. Computational results indicate that GPU-accelerated SIMT-ES-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid ES-PS with GPU acceleration.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a massively parallel Evolution Strategy &#8211; Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization functions. The algorithm is specifically designed for a graphic processing unit (GPU) hardware platform featuring &#8216;Single Instruction &#8211; Multiple Thread&#8217; (SIMT). GPU computing is an emerging desktop parallel computing platform. The [&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":[36,11,89,3],"tags":[1787,1782,14,969,20,615],"class_list":["post-2581","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-genetic-programming","tag-nvidia","tag-pattern-search"],"views":2023,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2581","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=2581"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2581\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2581"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2581"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2581"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}