{"id":10255,"date":"2013-08-08T23:54:00","date_gmt":"2013-08-08T20:54:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=10255"},"modified":"2013-08-08T23:54:00","modified_gmt":"2013-08-08T20:54:00","slug":"an-energy-optimization-of-a-gpu-application-by-grid-design-space-exploration","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10255","title":{"rendered":"An Energy Optimization of a GPU Application by Grid Design Space Exploration"},"content":{"rendered":"<p>Power and energy consumptions are also becoming important design criteria. Consequently, software designs have to consider the power\/energy consumptions together with performance when they are developing software. In this paper, we explore a design space exploration with a commercial GPU: nVidia GTX 660 for investigating the best configuration of a kernel grid structure in a GPU for optimal power or energy consumption.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Power and energy consumptions are also becoming important design criteria. Consequently, software designs have to consider the power\/energy consumptions together with performance when they are developing software. In this paper, we explore a design space exploration with a commercial GPU: nVidia GTX 660 for investigating the best configuration of a kernel grid structure in a [&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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,533,344,20,1436],"class_list":["post-10255","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-design-space-exploration","tag-energy-efficient-computing","tag-nvidia","tag-nvidia-geforce-gtx-660"],"views":2029,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10255","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=10255"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10255\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10255"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10255"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10255"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}