Automatic compilation for multiple types of devices is important, especially given the current trends towards heterogeneous computing. This paper concentrates on some issues in compiling fine-grained SPMD-threaded code (e.g., GPU CUDA code) for multicore CPUs. It points out some correctness pitfalls in existing techniques, particularly in their treatment to implicit synchronizations. It then describes a systematic dependence analysis specially designed for handling implicit synchronizations in SPMD-threaded programs. By unveiling the relations between inter-thread data dependences and correct treatment to synchronizations, it presents a dependence-based solution to the problem. Experiments demonstrate that the proposed techniques can resolve the correctness issues in existing compilation techniques, and help compilers produce correct and efficient translation results.<\/p>\n","protected":false},"excerpt":{"rendered":"
Automatic compilation for multiple types of devices is important, especially given the current trends towards heterogeneous computing. This paper concentrates on some issues in compiling fine-grained SPMD-threaded code (e.g., GPU CUDA code) for multicore CPUs. It points out some correctness pitfalls in existing techniques, particularly in their treatment to implicit synchronizations. It then describes a […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[955,1782,14,452,20],"class_list":["post-5752","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-compilers","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia"],"views":1838,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5752","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=5752"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5752\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5752"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5752"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5752"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}