{"id":8509,"date":"2012-11-16T23:15:43","date_gmt":"2012-11-16T21:15:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=8509"},"modified":"2012-11-16T23:15:43","modified_gmt":"2012-11-16T21:15:43","slug":"use-of-cuda-for-the-continuous-space-language-model","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8509","title":{"rendered":"Use of CUDA for the Continuous Space Language Model"},"content":{"rendered":"<p>The training phase of the Continuous Space Language Model (CSLM) was implemented in the NVIDIA hardware\/software architecture Compute Unified Device Architecture (CUDA).  Implementation was accomplished using a combination of CUBLAS library routines and CUDA kernel calls on three different CUDA enabled devices of varying compute capability and a time savings over the traditional CPU approach demonstrated.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The training phase of the Continuous Space Language Model (CSLM) was implemented in the NVIDIA hardware\/software architecture Compute Unified Device Architecture (CUDA). Implementation was accomplished using a combination of CUBLAS library routines and CUDA kernel calls on three different CUDA enabled devices of varying compute capability and a time savings over the traditional CPU approach [&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":[89,3,41],"tags":[238,14,20,1234,1789],"class_list":["post-8509","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-signal-processing","tag-cublas","tag-cuda","tag-nvidia","tag-nvidia-quadro-fx-2700-m","tag-signal-processing"],"views":2398,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8509","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=8509"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8509\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8509"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8509"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8509"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}