This paper tackles the challenges of obtaining more efficient data center computing while maintaining low latency, low cost, programmability, and the potential for workload consolidation. We introduce GNoM, a software framework enabling energy-efficient, latency bandwidth optimized UDP network and application processing on GPUs. GNoM handles the data movement and task management to facilitate the development of high-throughput UDP network services on GPUs. We use GNoM to develop MemcachedGPU, an accelerated key-value store, and evaluate the full system on contemporary hardware. MemcachedGPU achieves ~10 GbE line-rate processing of ~13 million requests per second (MRPS) while delivering an efficiency of 62 thousand RPS per Watt (KRPS\/W) on a high-performance GPU and 84.8 KRPS\/W on a lowpower GPU. This closely matches the throughput of an optimized FPGA implementation while providing up to 79% of the energy-efficiency on the low-power GPU. Additionally, the low-power GPU can potentially improve cost-efficiency (KRPS\/$) up to 17% over a state-of-the-art CPU implementation. At 8 MRPS, MemcachedGPU achieves a 95-percentile RTT latency under 300\u00b5s on both GPUs. An offline limit study on the low-power GPU suggests that MemcachedGPU may continue scaling throughput and energyefficiency up to 28.5 MRPS and 127 KRPS\/W respectively.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper tackles the challenges of obtaining more efficient data center computing while maintaining low latency, low cost, programmability, and the potential for workload consolidation. We introduce GNoM, a software framework enabling energy-efficient, latency bandwidth optimized UDP network and application processing on GPUs. GNoM handles the data movement and task management to facilitate the development […]<\/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":[11,89,3],"tags":[1782,14,667,344,20,1634,176,1390,1543],"class_list":["post-14467","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-databases","tag-energy-efficient-computing","tag-nvidia","tag-nvidia-geforce-gtx-750-ti","tag-package","tag-tesla-k20","tag-tesla-k40"],"views":2295,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14467","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=14467"}],"version-history":[{"count":2,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14467\/revisions"}],"predecessor-version":[{"id":14473,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14467\/revisions\/14473"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14467"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14467"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14467"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}