{"id":5653,"date":"2011-09-23T10:57:14","date_gmt":"2011-09-23T07:57:14","guid":{"rendered":"http:\/\/hgpu.org\/?p=5653"},"modified":"2011-09-23T10:57:14","modified_gmt":"2011-09-23T07:57:14","slug":"orchestration-by-approximation-mapping-stream-programs-onto-multicore-architectures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5653","title":{"rendered":"Orchestration by approximation: mapping stream programs onto multicore architectures"},"content":{"rendered":"<p>We present a novel 2-approximation algorithm for deploying stream graphs on multicore computers and a stream graph transformation that eliminates bottlenecks. The key technical insight is a data rate transfer model that enables the computation of a &quot;closed form&quot;, i.e., the data rate transfer function of an actor depending on the arrival rate of the stream program. A combinatorial optimization problem uses the closed form to maximize the throughput of the stream program. Although the problem is inherently NP-hard, we present an efficient and effective 2-approximation algorithm that provides a lower bound on the quality of the solution. We introduce a transformation that uses the closed form to identify and eliminate bottlenecks. We show experimentally that state-of-the art integer linear programming approaches for orchestrating stream graphs are (1) in-tractable or at least impractical for larger stream graphs and larger number of processors and (2) our 2-approximation algorithm is highly efficient and its results are close to the optimal solution for a standard set of StreamIt benchmark programs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a novel 2-approximation algorithm for deploying stream graphs on multicore computers and a stream graph transformation that eliminates bottlenecks. The key technical insight is a data rate transfer model that enables the computation of a &quot;closed form&quot;, i.e., the data rate transfer function of an actor depending on the arrival rate of the [&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":[36,11,3],"tags":[1787,451,955,1782,298,67,660],"class_list":["post-5653","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-benchmarking","tag-compilers","tag-computer-science","tag-optimization","tag-performance","tag-programming-languages"],"views":2170,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5653","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=5653"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5653\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5653"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5653"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5653"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}