{"id":16368,"date":"2016-08-01T23:55:31","date_gmt":"2016-08-01T20:55:31","guid":{"rendered":"http:\/\/hgpu.org\/?p=16368"},"modified":"2016-08-01T23:55:31","modified_gmt":"2016-08-01T20:55:31","slug":"automatic-loop-partitioning-for-heterogeneous-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16368","title":{"rendered":"Automatic Loop Partitioning for Heterogeneous Systems"},"content":{"rendered":"<p>In this work, we implement a tool that automatically partitions loops and then executes these partitions on heterogeneous systems. Partitioning a loop is the process of dividing a loop to form two or more new loops, each iterating over a portion of the original loops iteration space. A heterogeneous system is a system that is equipped with, and uses, different kinds of processing units. An example of this is a system that has access to a CPU and a GPU.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this work, we implement a tool that automatically partitions loops and then executes these partitions on heterogeneous systems. Partitioning a loop is the process of dividing a loop to form two or more new loops, each iterating over a portion of the original loops iteration space. A heterogeneous system is a system that is [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,452,20,1766,67,193,390],"class_list":["post-16368","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-titan-black","tag-performance","tag-ptx","tag-thesis"],"views":1938,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16368","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=16368"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16368\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16368"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16368"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16368"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}