Pipelining is a well-known approach to increasing parallelism and performance. We address the problem of software pipelining for heterogeneous parallel platforms that consist of different multi-core and many-core processing units. In this context, pipelining involves two key steps—partitioning an application into stages and mapping and scheduling the stages onto the processing units of the heterogeneous platform. We show that the inter-dependency between these steps is a critical challenge that must be addressed in order to achieve high performance. We propose an Automatic Heterogeneous Pipelining framework (ahp) that generates an optimized pipelined implementation of a program from an annotated unpipelined specification. Across three complex applications (image classification, object detection, and document retrieval) and two heterogeneous platforms (Intel Xeon multi-core CPUs with Intel MIC and NVIDIA GPGPU accelerators), ahp achieves a throughput improvement of up to 1.53x (1.37x on average) over a heterogeneous baseline that exploits data and task parallelism.<\/p>\n","protected":false},"excerpt":{"rendered":"
Pipelining is a well-known approach to increasing parallelism and performance. We address the problem of software pipelining for heterogeneous parallel platforms that consist of different multi-core and many-core processing units. In this context, pipelining involves two key steps—partitioning an application into stages and mapping and scheduling the stages onto the processing units of the heterogeneous […]<\/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":[1782,14,452,20,199],"class_list":["post-8541","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-tesla-c1060"],"views":1838,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8541","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=8541"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8541\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8541"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8541"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8541"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}