{"id":7171,"date":"2012-02-18T20:39:34","date_gmt":"2012-02-18T18:39:34","guid":{"rendered":"http:\/\/hgpu.org\/?p=7171"},"modified":"2012-02-18T20:39:34","modified_gmt":"2012-02-18T18:39:34","slug":"gpu-parallel-statistical-and-cube-test-analysis-of-the-sha-3-finalist-candidate-hash-functions","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7171","title":{"rendered":"GPU Parallel Statistical and Cube Test Analysis of the SHA-3 Finalist Candidate Hash Functions"},"content":{"rendered":"<p>The 256-bit versions of the SHA-3 finalist candidate hash functions &#8211; BLAKE, Grostl, JH, Keccak, and Skein &#8211; were subjected to statistical tests to attempt to disprove the hypothesis that the output bits are uniformly distributed, independent, binary random variables. The hash functions were also subjected to cube tests to attempt to disprove the hypothesis that the superpoly bits are uniformly distributed, independent, binary random variables. The hash functions and test programs were implemented to run in parallel on a 448-core GPU supercomputer; the cube tests in particular require massive amounts of computation and are ideally suited for parallel implementation. Nonrandom behavior was observed at the 0.01 significance level in the BLAKE, JH, Keccak, and Skein hash functions. Nonrandom behavior was not observed at the 0.01 significance level in the Grostl hash function.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The 256-bit versions of the SHA-3 finalist candidate hash functions &#8211; BLAKE, Grostl, JH, Keccak, and Skein &#8211; were subjected to statistical tests to attempt to disprove the hypothesis that the output bits are uniformly distributed, independent, binary random variables. The hash functions were also subjected to cube tests to attempt to disprove the hypothesis [&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":[11,89,3,287],"tags":[1782,14,132,20,176,1800,378],"class_list":["post-7171","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-security","tag-computer-science","tag-cuda","tag-hashing","tag-nvidia","tag-package","tag-security","tag-tesla-c2050"],"views":3173,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7171","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=7171"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7171\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7171"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7171"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7171"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}