In this paper, we introduce Heteroflow, a new C++ library to help developers quickly write parallel CPU-GPU programs using task dependency graphs. Heteroflow leverages the power of modern C++ and task-based approaches to enable efficient implementations of heterogeneous decomposition strategies. Our new CPU-GPU programming model allows users to express a problem in a way that adapts to effective separation of concerns and expertise encapsulation. Compared with existing libraries, Heteroflow is more cost-efficient in performance scaling, programming productivity, and solution generality. We have evaluated Heteroflow on two real applications in VLSI design automation and demonstrated the performance scalability across different CPU-GPU numbers and problem sizes. At a particular example of VLSI timing analysis with million-scale tasking, Heteroflow achieved 7.7x runtime speed-up (99 vs 13 minutes) over a baseline on a machine of 40 CPU cores and 4 GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we introduce Heteroflow, a new C++ library to help developers quickly write parallel CPU-GPU programs using task dependency graphs. Heteroflow leverages the power of modern C++ and task-based approaches to enable efficient implementations of heterogeneous decomposition strategies. Our new CPU-GPU programming model allows users to express a problem in a way that […]<\/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":[1782,14,452,20,2088,532],"class_list":["post-26469","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-2080","tag-timing-analysis"],"views":1567,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26469","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=26469"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/26469\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=26469"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=26469"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=26469"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}