{"id":6134,"date":"2011-11-01T17:58:22","date_gmt":"2011-11-01T15:58:22","guid":{"rendered":"http:\/\/hgpu.org\/?p=6134"},"modified":"2011-11-01T17:58:22","modified_gmt":"2011-11-01T15:58:22","slug":"using-drbl-to-deploy-mpich2-and-cuda-on-green-computing","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6134","title":{"rendered":"Using DRBL to Deploy MPICH2 and CUDA on Green Computing"},"content":{"rendered":"<p>In this paper, an energy efficient architecture for Build Energy Efficient GPU and CPU Cluster Using DRBL is proposed. This architecture helps administrator not only to quickly deploy and manage GPU and CPU Cluster environment, but also bring benefit of energy efficiency in scientific computing. The experiment simulates 3 cases to prove energy efficiency. We will compare GPU and CPU Cluster Using DRBL with the design without DRBL. According to the experiment results, the architecture provides a way to implement a power economization computing architecture that reduces energy consumption.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, an energy efficient architecture for Build Energy Efficient GPU and CPU Cluster Using DRBL is proposed. This architecture helps administrator not only to quickly deploy and manage GPU and CPU Cluster environment, but also bring benefit of energy efficiency in scientific computing. The experiment simulates 3 cases to prove energy efficiency. We [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_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},"jetpack_post_was_ever_published":false},"categories":[11,89,3],"tags":[1782,14,344,345,20,554],"class_list":["post-6134","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-green","tag-nvidia","tag-nvidia-geforce-9800-gt"],"views":2290,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6134","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=6134"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6134\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6134"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6134"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6134"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}