{"id":2556,"date":"2011-01-21T12:36:44","date_gmt":"2011-01-21T12:36:44","guid":{"rendered":"http:\/\/hgpu.org\/?p=2556"},"modified":"2011-01-21T12:36:44","modified_gmt":"2011-01-21T12:36:44","slug":"a-cuda-simt-interpreter-for-genetic-programming-revised","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2556","title":{"rendered":"A CUDA SIMT interpreter for genetic programming. Revised"},"content":{"rendered":"<p>A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of quarter of a million RPN expressions on graphics cards and nVidia Tesla T10P. Using sub-machine code GP a sustain peak performance of 212 billion GP operations per second (3300 speed up) and an average of 4.5 peta GP ops per day is reported for a single card on a Boolean induction benchmark never attempted before, let alone solved.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the whole GP population of quarter of a million RPN expressions on graphics cards and nVidia Tesla T10P. Using sub-machine code GP a sustain peak performance of 212 billion GP operations per second (3300 speed up) and an average of 4.5 peta [&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":[11,89,3],"tags":[1782,14,969,20,176,851],"class_list":["post-2556","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-genetic-programming","tag-nvidia","tag-package","tag-tesla-t10p"],"views":2432,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2556","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=2556"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2556\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2556"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2556"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2556"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}