{"id":6967,"date":"2012-01-19T14:06:42","date_gmt":"2012-01-19T12:06:42","guid":{"rendered":"http:\/\/hgpu.org\/?p=6967"},"modified":"2012-01-19T14:06:42","modified_gmt":"2012-01-19T12:06:42","slug":"asymptotic-peak-utilisation-in-heterogeneous-parallel-cpugpu-pipelines-a-decentralised-queue-monitoring-strategy","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6967","title":{"rendered":"Asymptotic Peak Utilisation in Heterogeneous Parallel CPU\/GPU Pipelines: A Decentralised Queue Monitoring Strategy"},"content":{"rendered":"<p>Heterogeneous parallel computing has become an unavoidable consequence of the emergence of GeneralPurpose computing on graphics processing units (GPGPU). The characteristics of a Graphics Processing Unit (GPU)-including significant memory transfer latency and complex performance characteristics-demand new approaches to ensuring that all available computational resources are geared towards optimal utilisation. This paper considers the simple case of a divisible workload based on widely-used numerical linear algebra routines and considers the challenges that present themselves when an attempt is made to efficiently use all resources available with a view in balancing the CPU and GPU utilisation. We suggest a possible queue monitoring strategy that facilitates resource usage for applications that fit the pipeline parallel architectural pattern on heterogeneous multicore\/multi-node CPU and GPU systems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Heterogeneous parallel computing has become an unavoidable consequence of the emergence of GeneralPurpose computing on graphics processing units (GPGPU). The characteristics of a Graphics Processing Unit (GPU)-including significant memory transfer latency and complex performance characteristics-demand new approaches to ensuring that all available computational resources are geared towards optimal utilisation. This paper considers the simple case [&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":[11,3],"tags":[1782,452,37,70],"class_list":["post-6967","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-heterogeneous-systems","tag-linear-algebra","tag-programming-techniques"],"views":1914,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6967","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=6967"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6967\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6967"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6967"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6967"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}