{"id":13884,"date":"2015-04-20T23:01:29","date_gmt":"2015-04-20T20:01:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=13884"},"modified":"2015-04-20T23:01:29","modified_gmt":"2015-04-20T20:01:29","slug":"verification-of-producer-consumer-synchronization-in-gpu-programs","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=13884","title":{"rendered":"Verification of Producer-Consumer Synchronization in GPU Programs"},"content":{"rendered":"<p>Previous efforts to formally verify code written for GPUs have focused solely on kernels written within the traditional data-parallel GPU programming model. No previous work has considered the higher performance, but more complex, warp-specialized kernels based on producer-consumer named barriers available on current hardware. In this work we present the first formal operational semantics for named barriers and define what it means for a warp-specialized kernel to be correct. We give algorithms for verifying the correctness of warp-specialized kernels and prove that they are both sound and complete for the most common class of warp-specialized programs. We also present WEFT, a verification tool for checking warp-specialized code. Using WEFT, we discover several non-trivial bugs in production warp-specialized kernels.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Previous efforts to formally verify code written for GPUs have focused solely on kernels written within the traditional data-parallel GPU programming model. No previous work has considered the higher performance, but more complex, warp-specialized kernels based on producer-consumer named barriers available on current hardware. In this work we present the first formal operational semantics for [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,20,176,193],"class_list":["post-13884","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-package","tag-ptx"],"views":2690,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13884","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=13884"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/13884\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=13884"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=13884"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=13884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}