{"id":8397,"date":"2012-10-22T23:26:26","date_gmt":"2012-10-22T20:26:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=8397"},"modified":"2012-10-22T23:26:26","modified_gmt":"2012-10-22T20:26:26","slug":"streaming-dynamic-coarse-grained-cpugpu-workloads-with-heterogeneous-pipelines-in-fastflow","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8397","title":{"rendered":"Streaming Dynamic Coarse-Grained CPU\/GPU Workloads with Heterogeneous Pipelines in FastFlow"},"content":{"rendered":"<p>Software pipelines permit the decomposition of a repetitive sequential process into a succession of distinguishable sub-processes called stages, each of which can be concurrently executed on a distinct processing element. This paper presents a heterogeneous streaming pipeline implementation using the FastFlow skeletal library for a numerical linear algebra code. By introducing minimal memory management, we implement a large-scale streaming application which allocates the different pipeline stages to multi-core CPU and multi-GPU resources in a cluster environment, demonstrating the suitability of the algorithmic skeleton approach to efficiently coordinate the pipeline operation. Our implementation shows that longrunning heterogeneous pipelines can be effectively implemented in FastFlow.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Software pipelines permit the decomposition of a repetitive sequential process into a succession of distinguishable sub-processes called stages, each of which can be concurrently executed on a distinct processing element. This paper presents a heterogeneous streaming pipeline implementation using the FastFlow skeletal library for a numerical linear algebra code. By introducing minimal memory management, we [&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":[11,89,3],"tags":[1782,14,452,37,20,1241],"class_list":["post-8397","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-linear-algebra","tag-nvidia","tag-tesla-m2090"],"views":2138,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8397","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=8397"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8397\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8397"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8397"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8397"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}