We propose and evaluate a novel strategy for tuning the performance of a class of stencil computations on Graphics Processing Units. The strategy uses a machine learning model to predict the optimal way to load data from memory followed by a heuristic that divides other optimizations into groups and exhaustively explores one group at a time. We use a set of 104 synthetic OpenCL stencil benchmarks that are representative of many real stencil computations. We first demonstrate the need for auto-tuning by showing that the optimization space is sufficiently complex that simple approaches to determining a high-performing configuration fail. We then demonstrate the effectiveness of our approach on NVIDIA and AMD GPUs. Relative to a random sampling of the space, we find configurations that are 12%\/32% faster on the NVIDIA\/AMD platform in 71% and 4% less time respectively. Relative to an expert search, we achieve 5% and 9% better performance on the two platforms in 89% and 76% less time. We also evaluate our strategy for different stencil computational intensities, varying array sizes and shapes, and in combination with expert search.<\/p>\n","protected":false},"excerpt":{"rendered":"
We propose and evaluate a novel strategy for tuning the performance of a class of stencil computations on Graphics Processing Units. The strategy uses a machine learning model to predict the optimal way to load data from memory followed by a heuristic that divides other optimizations into groups and exhaustively explores one group at a […]<\/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":[1904,7,1782,1025,20,1470,1793,67,1728],"class_list":["post-18184","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-amd-radeon-r9-390","tag-ati","tag-computer-science","tag-machine-learning","tag-nvidia","tag-nvidia-geforce-gtx-titan","tag-opencl","tag-performance","tag-stencil-computation"],"views":2568,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18184","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=18184"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/18184\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=18184"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=18184"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=18184"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}