Recently, CPU-GPU heterogeneous parallelism has brought transformational performance milestones to static timing analysis (STA) algorithms. As the computing ecosystem continues to proliferate, performance portability has emerged as a new challenge when deploying the result to diverse heterogeneous computing platforms. Specifically, the optimal code written on a CPU-GPU architecture may not be optimal for other CPUGPU architectures, due to various performance, interoperability, and availability constraints. As a result, we introduce in this paper a learning-based framework to enhance the performance portability of a GPU-accelerated STA program. We parameterize important performance parameters and leverage a neural network model to adapt performance optimization to any given computing platforms. We have demonstrated the effectiveness of our framework in real STA applications.<\/p>\n","protected":false},"excerpt":{"rendered":"
Recently, CPU-GPU heterogeneous parallelism has brought transformational performance milestones to static timing analysis (STA) algorithms. As the computing ecosystem continues to proliferate, performance portability has emerged as a new challenge when deploying the result to diverse heterogeneous computing platforms. Specifically, the optimal code written on a CPU-GPU architecture may not be optimal for other CPUGPU […]<\/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":[2112,7,1782,1673,20,2082,1793,1586,2020,1845],"class_list":["post-27215","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-amd-radeon-rx-6900-xt","tag-ati","tag-computer-science","tag-deep-learning","tag-nvidia","tag-nvidia-geforce-rtx-3090","tag-opencl","tag-performance-portability","tag-pytorch","tag-sycl"],"views":1271,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/27215","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=27215"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/27215\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=27215"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=27215"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=27215"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}