{"id":2900,"date":"2011-02-19T14:59:40","date_gmt":"2011-02-19T14:59:40","guid":{"rendered":"http:\/\/hgpu.org\/?p=2900"},"modified":"2011-02-19T14:59:40","modified_gmt":"2011-02-19T14:59:40","slug":"efficiency-considerations-of-cauchy-reed-solomon-implementations-on-accelerator-and-multi-core-platforms","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2900","title":{"rendered":"Efficiency Considerations of Cauchy Reed-Solomon Implementations on Accelerator and Multi-Core Platforms"},"content":{"rendered":"<p>The Cauchy variant of the Reed-Solomon algorithm is implemented on accelerator platforms including GPGPU, FPGA, CellBE and ClearSpeed as well as on a x86 multi-core system. The sustained throughput performance and kernel rates are measured for a 5+3 Reed-Solomon schema. To compare the different technology platforms an efficiency is introduced and the platforms are categorized according to their Reed-Solomon efficiency.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Cauchy variant of the Reed-Solomon algorithm is implemented on accelerator platforms including GPGPU, FPGA, CellBE and ClearSpeed as well as on a x86 multi-core system. The sustained throughput performance and kernel rates are measured for a 5+3 Reed-Solomon schema. To compare the different technology platforms an efficiency is introduced and the platforms are categorized [&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":[545,1782,14,272,377,20,199,202],"class_list":["post-2900","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-cell-processor","tag-computer-science","tag-cuda","tag-error-recovery","tag-fpga","tag-nvidia","tag-tesla-c1060","tag-tesla-c870"],"views":2464,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2900","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=2900"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2900\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2900"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2900"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2900"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}