{"id":12308,"date":"2014-06-18T09:48:15","date_gmt":"2014-06-18T06:48:15","guid":{"rendered":"http:\/\/hgpu.org\/?p=12308"},"modified":"2014-06-18T09:48:15","modified_gmt":"2014-06-18T06:48:15","slug":"computing-on-knights-and-kepler-architectures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=12308","title":{"rendered":"Computing on Knights and Kepler Architectures"},"content":{"rendered":"<p>A recent trend in scientific computing is the increasingly important role of co-processors, originally built to accelerate graphics rendering, and now used for general high-performance computing. The INFN Computing On Knights and Kepler Architectures (COKA) project focuses on assessing the suitability of co-processor boards for scientific computing in a wide range of physics applications, and on studying the best programming methodologies for these systems. Here we present in a comparative way our results in porting a Lattice Boltzmann code on two state-of-the-art accelerators: the NVIDIA K20X, and the Intel Xeon-Phi. We describe our implementations, analyze results and compare with a baseline architecture adopting Intel Sandy Bridge CPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A recent trend in scientific computing is the increasingly important role of co-processors, originally built to accelerate graphics rendering, and now used for general high-performance computing. The INFN Computing On Knights and Kepler Architectures (COKA) project focuses on assessing the suitability of co-processor boards for scientific computing in a wide range of physics applications, and [&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":[89,104,3],"tags":[14,1795,1483,108,20,1390],"class_list":["post-12308","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-fluid-dynamics","category-paper","tag-cuda","tag-fluid-dynamics","tag-intel-xeon-phi","tag-lattice-boltzmann-model","tag-nvidia","tag-tesla-k20"],"views":1996,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12308","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=12308"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12308\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12308"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12308"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12308"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}