{"id":8729,"date":"2013-01-03T23:56:34","date_gmt":"2013-01-03T21:56:34","guid":{"rendered":"http:\/\/hgpu.org\/?p=8729"},"modified":"2013-01-03T23:56:34","modified_gmt":"2013-01-03T21:56:34","slug":"ubench-performance-impact-of-cuda-block-geometry","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8729","title":{"rendered":"uBench: Performance Impact of CUDA Block Geometry"},"content":{"rendered":"<p>Nowadays, there is a lack of performance models for the execution of programs implemented using the CUDA model for GPU (Graphics Processing Units) devices. We have designed and implemented a suite of micro-benchmarks, called uBench. The purpose of uBench is to identify the effects on performance derived from the combination of: (1) the hardware details of each GPU device, and (2) the configuration of the thread-block, an important runtime configuration parameter in the CUDA programming model.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Nowadays, there is a lack of performance models for the execution of programs implemented using the CUDA model for GPU (Graphics Processing Units) devices. We have designed and implemented a suite of micro-benchmarks, called uBench. The purpose of uBench is to identify the effects on performance derived from the combination of: (1) the hardware details [&hellip;]<\/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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[451,1782,14,20,379,1306,67],"class_list":["post-8729","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-benchmarking","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-nvidia-geforce-gtx-680","tag-performance"],"views":2143,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8729","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=8729"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8729\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8729"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8729"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8729"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}