{"id":5548,"date":"2011-09-12T15:08:36","date_gmt":"2011-09-12T12:08:36","guid":{"rendered":"http:\/\/hgpu.org\/?p=5548"},"modified":"2011-09-12T15:08:36","modified_gmt":"2011-09-12T12:08:36","slug":"energy-efficient-computing-for-extreme-scale-science","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5548","title":{"rendered":"Energy-efficient computing for extreme-scale science"},"content":{"rendered":"<p>A many-core processor design for high-performance systems draws from embedded computing&#8217;s low-power architectures and design processes, providing a radical alternative to cluster solutions. The computational power required to accurately model extreme problem spaces, such as climate change, requires more than a business-as-usual approach. Building ever-larger clusters of commercial off-the-shelf (COTS) hardware will be increasingly constrained by power and cooling-with power consumption projected to be hundreds of megawatts for exascale-class problems according to recent DARPA and DOE reports. It makes more sense therefore to leverage the considerable innovation of the low-power architectures developed for embedded computing markets and design a machine capable of the exaf lops performance (1 billion-billion floating-point operations per second) required for this and similarly demanding scientific applications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A many-core processor design for high-performance systems draws from embedded computing&#8217;s low-power architectures and design processes, providing a radical alternative to cluster solutions. The computational power required to accurately model extreme problem spaces, such as climate change, requires more than a business-as-usual approach. Building ever-larger clusters of commercial off-the-shelf (COTS) hardware will be increasingly constrained [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":[11,3],"tags":[1782,344,31],"class_list":["post-5548","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-energy-efficient-computing","tag-review"],"views":1814,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5548","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=5548"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5548\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}