Hardware accelerators have become the backbone of many cloud and HPC workloads, but workloads tend to statically choose accelerators leaving devices unused while others are oversubscribed. We propose a holistic framework that allows a computational kernel to span across multiple devices on a node, as well as multiple applications being scheduled on the same node. Our work sharing and co-scheduling framework allows kernels to be migrated between devices, expand to span more devices, or contract to fewer devices. The scheduler can make these decisions dynamically based on a pluggable scheduling algorithm in order to optimize for different objectives, e.g., job throughput, job priorities or some hybrid. Experiments on a CPU+GPU+FPGA platform indicate speedups of 2.26X over different applications and up to 1.25X for co-scheduled workloads over baselines. Besides performance, a major contribution of our work lies in ease of programmability with a single code base compiled and runtime controlled across three vastly different execution devices.<\/p>\n","protected":false},"excerpt":{"rendered":"
Hardware accelerators have become the backbone of many cloud and HPC workloads, but workloads tend to statically choose accelerators leaving devices unused while others are oversubscribed. We propose a holistic framework that allows a computational kernel to span across multiple devices on a node, as well as multiple applications being scheduled on the same node. […]<\/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":[11,90,3],"tags":[1782,377,452,20,2058,1793,854],"class_list":["post-29213","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-fpga","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-rtx-2060","tag-opencl","tag-task-scheduling"],"views":1711,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29213","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=29213"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/29213\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=29213"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=29213"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=29213"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}