{"id":7661,"date":"2012-05-26T23:02:29","date_gmt":"2012-05-26T20:02:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=7661"},"modified":"2012-05-26T23:02:29","modified_gmt":"2012-05-26T20:02:29","slug":"parameterized-verification-of-gpu-kernel-programs","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7661","title":{"rendered":"Parameterized Verification of GPU Kernel Programs"},"content":{"rendered":"<p>We present an automated symbolic verifier for checking the functional correctness of GPGPU kernels parametrically, for an arbitrary number of threads. Our tool PUGpara checks the functional equivalence of a kernel and its optimized versions, helping debug errors introduced during memory coalescing and bank conflict elimination related optimizations. Key features of our work include: (1) a symbolic method to encode a comparative assertion across two kernel versions, and (2) techniques to overcome SMT solver restrictions through overapproximations, yielding an efficient bug-hunting method.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present an automated symbolic verifier for checking the functional correctness of GPGPU kernels parametrically, for an arbitrary number of threads. Our tool PUGpara checks the functional equivalence of a kernel and its optimized versions, helping debug errors introduced during memory coalescing and bank conflict elimination related optimizations. Key features of our work include: (1) [&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":[1782,14,20,298],"class_list":["post-7661","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-optimization"],"views":2022,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7661","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=7661"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7661\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}