GPU (General-Purpose computation on Graphics Processing Units) offers an effective computing platform to accelerate a wide class of data-parallel computing. Multi-GPU’s appear as an attractive platform to speed up the computation of data-parallel GPU. This paper aims to explore the feasibility of relaxing the task-level restriction of single GPU use in multi-GPU real-time systems.We develop a multi-GPU runtime support system, called GPU-SPARC, where GPU applications can be automatically split and run concurrently over multi-GPU’s. We present the prototype of GPU-SPARC on OpenCL runtime that can provide the service to existing OpenCL applications without any modification to them unless global synchronization is employed. The multi-GPU parallel computing offers the potential for performance improvement but at the same time incurs additional resource consumption. Thereby, we analysis the benefit and cost of executing a GPU application on multiple GPU’s and propose a GPU execution mode assignment policy from the perspective of system-wide schedulability. Our experiment results show that GPU-SPARC is able to improve schedulability in real-time multi-GPU systems by relaxing the single-GPU-per-task restriction and choosing better GPU execution modes.<\/p>\n","protected":false},"excerpt":{"rendered":"
GPU (General-Purpose computation on Graphics Processing Units) offers an effective computing platform to accelerate a wide class of data-parallel computing. Multi-GPU’s appear as an attractive platform to speed up the computation of data-parallel GPU. This paper aims to explore the feasibility of relaxing the task-level restriction of single GPU use in multi-GPU real-time systems.We develop […]<\/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":[1782,20,1089,1793,176,67],"class_list":["post-12653","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-nvidia","tag-nvidia-geforce-gtx-560-ti","tag-opencl","tag-package","tag-performance"],"views":2880,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12653","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=12653"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/12653\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12653"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12653"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12653"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}