Computer systems are increasingly heterogeneous with nodes consisting of CPUs and GPU accelerators. As such systems become mainstream, they move away from specialized highperformance single application platforms to a more general setting with multiple, concurrent, application jobs. Determining how jobs should be dynamically best scheduled to heterogeneous devices is non-trivial. In certain cases, performance is maximized if jobs are allocated to a single device, in others, sharing is preferable. In this paper, we present a runtime framework which schedules multi-user OpenCL tasks to their most suitable device in a CPU\/GPU system. We use a machine learning-based predictive model at runtime to detect whether to merge OpenCL kernels or schedule them separately to the most appropriate devices without the need for ahead-of-time profiling. We evaluate out approach over a wide range of workloads, on two separate platforms. We consistently show significant performance and turn-around time improvement over the state-of-the-art across programs, workload, and platforms.<\/p>\n","protected":false},"excerpt":{"rendered":"
Computer systems are increasingly heterogeneous with nodes consisting of CPUs and GPU accelerators. As such systems become mainstream, they move away from specialized highperformance single application platforms to a more general setting with multiple, concurrent, application jobs. Determining how jobs should be dynamically best scheduled to heterogeneous devices is non-trivial. In certain cases, performance is […]<\/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":[11,90,3],"tags":[1636,7,1782,452,1025,20,1504,1793],"class_list":["post-17110","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-amd-radeon-hd-7970","tag-ati","tag-computer-science","tag-heterogeneous-systems","tag-machine-learning","tag-nvidia","tag-nvidia-geforce-gtx-780","tag-opencl"],"views":2385,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17110","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=17110"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17110\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17110"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17110"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17110"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}