{"id":4788,"date":"2011-07-17T17:05:46","date_gmt":"2011-07-17T14:05:46","guid":{"rendered":"http:\/\/hgpu.org\/?p=4788"},"modified":"2011-07-17T17:05:46","modified_gmt":"2011-07-17T14:05:46","slug":"arbitrary-dimension-reed-solomon-coding-and-decoding-for-extended-raid-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4788","title":{"rendered":"Arbitrary dimension Reed-Solomon coding and decoding for extended RAID on GPUs"},"content":{"rendered":"<p>Reed-Solomon coding is a method of generating arbitrary amounts of checksum information from original data via matrix-vector multiplication in finite fields. Previous work has shown that CPUs are not well-matched to this type of computation, but recent graphical processing units (GPUs) have been shown through a case study to perform this encoding quickly for the 3 + 3 (three data + three parity) case. In order to be utilized in a true RAID-like system, it is important to understand how well this computation can scale in the number of data disks supported. This paper details the performance of a general Reed-Solomon encoding and decoding library that is suitable for use in RAID-like systems. Both generation and recovery are performance-tested and discussed.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Reed-Solomon coding is a method of generating arbitrary amounts of checksum information from original data via matrix-vector multiplication in finite fields. Previous work has shown that CPUs are not well-matched to this type of computation, but recent graphical processing units (GPUs) have been shown through a case study to perform this encoding quickly for the [&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":[1782,14,272,20,253,1800],"class_list":["post-4788","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-security","tag-computer-science","tag-cuda","tag-error-recovery","tag-nvidia","tag-nvidia-geforce-gtx-260","tag-security"],"views":2647,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4788","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=4788"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4788\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4788"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4788"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4788"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}