{"id":16277,"date":"2016-07-20T23:38:01","date_gmt":"2016-07-20T20:38:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=16277"},"modified":"2016-07-20T23:38:01","modified_gmt":"2016-07-20T20:38:01","slug":"scientific-computing-using-consumer-video-gaming-hardware-devices","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16277","title":{"rendered":"Scientific Computing Using Consumer Video-Gaming Hardware Devices"},"content":{"rendered":"<p>Commodity video-gaming hardware (consoles, graphics cards, tablets, etc.) performance has been advancing at a rapid pace owing to strong consumer demand and stiff market competition. Gaming hardware devices are currently amongst the most powerful and cost-effective computational technologies available in quantity. In this article, we evaluate a sample of current generation video-gaming hardware devices for scientific computing and compare their performance with specialized supercomputing general purpose graphics processing units (GPGPUs). We use the OpenCL SHOC benchmark suite, which is a measure of the performance of compute hardware on various different scientific application kernels, and also a popular public distributed computing application, Einstein@Home in the field of gravitational physics for the purposes of this evaluation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Commodity video-gaming hardware (consoles, graphics cards, tablets, etc.) performance has been advancing at a rapid pace owing to strong consumer demand and stiff market competition. Gaming hardware devices are currently amongst the most powerful and cost-effective computational technologies available in quantity. In this article, we evaluate a sample of current generation video-gaming hardware devices for [&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":[11,90,3],"tags":[1891,7,451,1782,20,1884,1793,176,67,1543],"class_list":["post-16277","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-amd-radeon-r9-fury-x","tag-ati","tag-benchmarking","tag-computer-science","tag-nvidia","tag-nvidia-tegra-tx1","tag-opencl","tag-package","tag-performance","tag-tesla-k40"],"views":2375,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16277","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=16277"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16277\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}