{"id":6643,"date":"2011-12-20T16:46:41","date_gmt":"2011-12-20T14:46:41","guid":{"rendered":"http:\/\/hgpu.org\/?p=6643"},"modified":"2011-12-20T16:46:41","modified_gmt":"2011-12-20T14:46:41","slug":"parsing-in-parallel-on-multiple-cores-and-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6643","title":{"rendered":"Parsing in Parallel on Multiple Cores and GPUs"},"content":{"rendered":"<p>This paper examines the ways in which parallelism can be used to speed the parsing of dense PCFGs. We focus on two kinds of parallelism here: Symmetric Multi-Processing (SMP) parallelism on shared-memory multicore CPUs, and Single-Instruction MultipleThread (SIMT) parallelism on GPUs. We describe how to achieve speed-ups over an already very efficient baseline parser using both kinds of technology. For our dense PCFG parsing task we obtained a 60x speed-up using SMP and SSE parallelism coupled with a cache-sensitive algorithm design, parsing section 24 of the Penn WSJ treebank in a little over 2 secs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper examines the ways in which parallelism can be used to speed the parsing of dense PCFGs. We focus on two kinds of parallelism here: Symmetric Multi-Processing (SMP) parallelism on shared-memory multicore CPUs, and Single-Instruction MultipleThread (SIMT) parallelism on GPUs. We describe how to achieve speed-ups over an already very efficient baseline parser using [&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":[1787,1782,14,20,1035],"class_list":["post-6643","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-nvidia","tag-tesla-s2050"],"views":1861,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6643","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=6643"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6643\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6643"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6643"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6643"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}