{"id":14813,"date":"2015-11-04T22:50:08","date_gmt":"2015-11-04T20:50:08","guid":{"rendered":"http:\/\/hgpu.org\/?p=14813"},"modified":"2015-11-04T22:50:08","modified_gmt":"2015-11-04T20:50:08","slug":"performance-of-gtx-titan-x-gpus-and-code-optimization","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=14813","title":{"rendered":"Performance of GTX Titan X GPUs and Code Optimization"},"content":{"rendered":"<p>Recently Nvidia has released a new GPU model: GTX Titan X (TX) in a linage of the Maxwell architecture. We use our conjugate gradient code and non-perturbative renormalization code to measure the performance of TX. The results are compared with those of GTX Titan Black (TB) in a lineage of the Kepler architecture. We observe a significant gain in the single and double precision calculations much greater than the theoretical expectation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Recently Nvidia has released a new GPU model: GTX Titan X (TX) in a linage of the Maxwell architecture. We use our conjugate gradient code and non-perturbative renormalization code to measure the performance of TX. The results are compared with those of GTX Titan Black (TB) in a lineage of the Kepler architecture. We observe [&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":[11,89,3],"tags":[1782,14,20,1767,67],"class_list":["post-14813","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-titan-x","tag-performance"],"views":2732,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14813","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=14813"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14813\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14813"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14813"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14813"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}