{"id":3633,"date":"2011-04-18T21:36:04","date_gmt":"2011-04-18T21:36:04","guid":{"rendered":"http:\/\/hgpu.org\/?p=3633"},"modified":"2011-04-18T21:36:04","modified_gmt":"2011-04-18T21:36:04","slug":"many-core-vs-many-thread-machines-stay-away-from-the-valley","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3633","title":{"rendered":"Many-Core vs. Many-Thread Machines: Stay Away From the Valley"},"content":{"rendered":"<p>We study the tradeoffs between many-core machines like Intel&#8217;s Larrabee and many-thread machines like Nvidia and AMD GPGPUs. We define a unified model describing a superposition of the two architectures, and use it to identify operation zones for which each machine is more suitable. Moreover, we identify an intermediate zone in which both machines deliver inferior performance. We study the shape of this ldquoperformance valleyrdquo and provide insights on how it can be avoided.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We study the tradeoffs between many-core machines like Intel&#8217;s Larrabee and many-thread machines like Nvidia and AMD GPGPUs. We define a unified model describing a superposition of the two architectures, and use it to identify operation zones for which each machine is more suitable. Moreover, we identify an intermediate zone in which both machines deliver [&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":[7,1782,905,820,20,67],"class_list":["post-3633","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-ati","tag-computer-science","tag-intel","tag-larrabee","tag-nvidia","tag-performance"],"views":2256,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3633","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=3633"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3633\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3633"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3633"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3633"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}