This paper describes the first implementation of Andersen’s inclusion-based pointer analysis for C programs on a heterogeneous CPU-GPU system, where both its CPU and GPU cores are used. As an important graph algorithm, Andersen’s analysis is difficult to parallelise because it makes extensive modifications to the structure of the underlying graph, in a way that is highly input-dependent and statically hard to analyse. Existing parallel solutions run on either the CPU or GPU but not both, rendering the underlying computational resources underutilised and the ratios of CPU-only over GPU-only speedups for certain programs (i.e., graphs) unpredictable. We observe that a naive parallel solution of Andersen’s analysis on a CPU-GPU system suffers from poor performance due to workload imbalance. We introduce a solution that is centered around a new dynamic workload distribution scheme. The novelty lies in prioritising the distribution of different types of workloads, i.e., graph-rewriting rules in Andersen’s analysis to CPU or GPU according to the degrees of the processing unit’s suitability for processing them. This scheme is effective when combined with synchronisation-free execution of tasks (i.e., graph-rewriting rules) and difference propagation of points-to information between the CPU and GPU. For a set of seven C benchmarks evaluated, our CPU-GPU solution outperforms (on average) (1) the CPU-only solution by 50.6%, (2) the GPU-only solution by 78.5%, and (3) an oracle solution that behaves as the faster of (1) and (2) on every benchmark by 34.6%.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper describes the first implementation of Andersen’s inclusion-based pointer analysis for C programs on a heterogeneous CPU-GPU system, where both its CPU and GPU cores are used. As an important graph algorithm, Andersen’s analysis is difficult to parallelise because it makes extensive modifications to the structure of the underlying graph, in a way that […]<\/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":[36,11,89,3],"tags":[1787,1782,14,452,20,144,1390],"class_list":["post-10836","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-rendering","tag-tesla-k20"],"views":2414,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10836","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=10836"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10836\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10836"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10836"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10836"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}