{"id":2527,"date":"2011-01-18T13:26:49","date_gmt":"2011-01-18T13:26:49","guid":{"rendered":"http:\/\/hgpu.org\/?p=2527"},"modified":"2011-01-18T13:26:49","modified_gmt":"2011-01-18T13:26:49","slug":"blasting-through-lattice-calculations-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2527","title":{"rendered":"Blasting through lattice calculations using CUDA"},"content":{"rendered":"<p>Modern graphics hardware is designed for highly parallel numerical tasks and provides significant cost and performance benefits. Graphics hardware vendors are now making available development tools to support general purpose high performance computing. Nvidia&#8217;s CUDA platform, in particular, offers direct access to graphics hardware through a programming language similar to C. Using the CUDA platform we have implemented a Wilson-Dirac operator which runs at an effective 68 Gflops on the Tesla C870. The recently released GeForce GTX 280 runs this same code at 92 Gflops, and we expect further improvement pending code optimization.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modern graphics hardware is designed for highly parallel numerical tasks and provides significant cost and performance benefits. Graphics hardware vendors are now making available development tools to support general purpose high performance computing. Nvidia&#8217;s CUDA platform, in particular, offers direct access to graphics hardware through a programming language similar to C. Using the CUDA platform [&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":[89,3,12],"tags":[14,110,72,20,234,1783,335,202],"class_list":["post-2527","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-cuda","tag-high-energy-physics-lattice","tag-monte-carlo-simulation","tag-nvidia","tag-nvidia-geforce-gtx-280","tag-physics","tag-qcd","tag-tesla-c870"],"views":2101,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2527","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=2527"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2527\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2527"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2527"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2527"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}