{"id":12035,"date":"2014-05-10T01:35:49","date_gmt":"2014-05-09T22:35:49","guid":{"rendered":"http:\/\/hgpu.org\/?p=12035"},"modified":"2014-05-10T01:35:49","modified_gmt":"2014-05-09T22:35:49","slug":"a-study-of-the-parallelization-of-hybrid-sat-solver-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12035","title":{"rendered":"A Study of the Parallelization of Hybrid SAT Solver using CUDA"},"content":{"rendered":"<p>SAT solver is an algorithm for finding the solution of a given problem by using CNF (Conjunctive Normal Form). Recently SAT solver studies have focused on the aspect of cryptography. The purpose of this paper is to construct the framework of a parallel SAT solver that can be applied to cryptanalysis. First, we transform an algebraic equation of the reduced AES(Advanced Encryption Standard) into CNF and then, analyze its properties and design a parallel SAT solver for cryptanalysis. Second, we implement a hybrid SAT solver that combines a complete SAT solver and an incomplete SAT solver. minisat-2.2.0 is used by the complete SAT solver and greedy SAT, by the incomplete SAT solver. Finally, we parallelize the hybrid SAT solver using NVIDIA&#8217;s CUDA to analyze the CNF of the reduced AES. In conclusion, we have constructed a framework that can develop various SAT solver applied parallelization strategies by using CUDA in the hybrid SAT solver. We will apply to this method for CNFs of small-scale AESs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>SAT solver is an algorithm for finding the solution of a given problem by using CNF (Conjunctive Normal Form). Recently SAT solver studies have focused on the aspect of cryptography. The purpose of this paper is to construct the framework of a parallel SAT solver that can be applied to cryptanalysis. First, we transform an [&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":[36,11,89,3,287],"tags":[1787,1782,14,20,1436,751,1800],"class_list":["post-12035","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","category-security","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-660","tag-sat","tag-security"],"views":2396,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12035","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=12035"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12035\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12035"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12035"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12035"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}