{"id":14015,"date":"2015-05-19T01:29:06","date_gmt":"2015-05-18T22:29:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=14015"},"modified":"2015-05-19T01:29:06","modified_gmt":"2015-05-18T22:29:06","slug":"an-interrupt-driven-work-sharing-for-loop-scheduler","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=14015","title":{"rendered":"An Interrupt-Driven Work-Sharing For-Loop Scheduler"},"content":{"rendered":"<p>In this paper we present a parallel for-loop scheduler which is based on work-stealing principles but runs under a completely cooperative scheme. POSIX signals are used by idle threads to interrupt left-behind workers, which in turn decide what portion of their workload can be given to the requester. We call this scheme Interrupt-Driven Work-Sharing (IDWS). This article describes how IDWS works, how it can be integrated into any POSIX-compliant OpenMP implementation and how a user can manually replace OpenMP parallel for-loops with IDWS in existing POSIX-compliant C++ applications. Additionally, we measure its performance using both a synthetic benchmark with varying distributions of workload across the iteration space and a real-life application on Sandy Bridge and Xeon Phi systems. Regardless the workload distribution and the underlying hardware, IDWS is always the best or among the best-performing strategies, providing a good all-around solution to the scheduling-choice dilemma.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present a parallel for-loop scheduler which is based on work-stealing principles but runs under a completely cooperative scheme. POSIX signals are used by idle threads to interrupt left-behind workers, which in turn decide what portion of their workload can be given to the requester. We call this scheme Interrupt-Driven Work-Sharing (IDWS). [&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":[11,3],"tags":[1782,1483,252,176],"class_list":["post-14015","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-intel-xeon-phi","tag-openmp","tag-package"],"views":2112,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14015","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=14015"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14015\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14015"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14015"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14015"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}