{"id":9634,"date":"2013-06-22T23:58:45","date_gmt":"2013-06-22T20:58:45","guid":{"rendered":"http:\/\/hgpu.org\/?p=9634"},"modified":"2013-06-22T23:58:45","modified_gmt":"2013-06-22T20:58:45","slug":"exploring-gpgpus-workload-characteristics-and-power-consumption","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=9634","title":{"rendered":"Exploring GPGPUs Workload Characteristics and Power Consumption"},"content":{"rendered":"<p>While general purpose computing on GPUs continues to enjoy higher computing performance with every new generation. The high power consumption of GPUs is an increasingly important concern. To create power-efficient GPUs, it is important to thoroughly study its power consumption. The power consumption of GPUs varies significantly with workloads. Therefore, in this work we study GPU power consumption at a detailed level and its correlation with well-known workload characteristics such as IPC. The low IPC kernels are further explored for the possible bottlenecks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>While general purpose computing on GPUs continues to enjoy higher computing performance with every new generation. The high power consumption of GPUs is an increasingly important concern. To create power-efficient GPUs, it is important to thoroughly study its power consumption. The power consumption of GPUs varies significantly with workloads. Therefore, in this work we study [&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":[1782,14,344,1398,20,974,67],"class_list":["post-9634","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-energy-efficient-computing","tag-gpgpu-sim","tag-nvidia","tag-nvidia-geforce-gtx-580","tag-performance"],"views":1965,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9634","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=9634"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9634\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9634"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9634"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9634"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}