Coprocessors such as GPUs are increasingly being deployed in clusters to process scientific and compute-intensive jobs. In this work, we study if GPU-based heterogeneous clusters can benefit client-server applications. Specifically, we consider the practical situation where multiple client-server applications share a heterogeneous cluster (multi-tenancy), and experience unpredictable variations in incoming client request rates, including steep load spikes. Even for "compute-intensive" client-server applications, it is unclear if a GPU-based cluster can seamlessly deliver acceptable response times in the presence of multi-tenancy and load spikes. We argue that a cluster-level scheduler that is aware of application load, request deadlines and the heterogeneity is necessary in this situation. We propose a novel scheduler called Symphony that enables efficient, dynamic sharing of a GPU-based heterogeneous cluster across multiple concurrently-executing client-server applications, each with arbitrary load spikes. Symphony performs three key tasks: it (i) monitors the load on each application, (ii) collects past performance data and dynamically builds simple performance models of available processing resources and (iii) computes a priority for pending requests based on the above parameters and the requests’ slack. Based on this, it reorders client requests across different applications to achieve acceptable response times. We also define how client-server applications should interact with a scheduler such as Symphony, and develop an API to this end. We deploy Symphony as user-space middleware on a high-end heterogeneous cluster with dual quad-core Xeon CPUs and dual NVIDIA Fermi GPUs. An evaluation using representative applications shows that in the presence of load spikes (i) Symphony incurs 2-20x fewer requests that do not meet response time constraints compared with other schedulers, and (ii) in order to achieve the same performance as Symphony, other scheduler- – s need 2x more cluster nodes.<\/p>\n","protected":false},"excerpt":{"rendered":"
Coprocessors such as GPUs are increasingly being deployed in clusters to process scientific and compute-intensive jobs. In this work, we study if GPU-based heterogeneous clusters can benefit client-server applications. Specifically, we consider the practical situation where multiple client-server applications share a heterogeneous cluster (multi-tenancy), and experience unpredictable variations in incoming client request rates, including steep […]<\/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":[589,1782,14,106,452,20,854,378],"class_list":["post-6383","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-cluster-computing","tag-computer-science","tag-cuda","tag-gpu-cluster","tag-heterogeneous-systems","tag-nvidia","tag-task-scheduling","tag-tesla-c2050"],"views":1881,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6383","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=6383"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6383\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6383"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6383"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6383"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}