{"id":7389,"date":"2012-04-04T22:28:04","date_gmt":"2012-04-04T19:28:04","guid":{"rendered":"http:\/\/hgpu.org\/?p=7389"},"modified":"2012-04-04T22:28:04","modified_gmt":"2012-04-04T19:28:04","slug":"fine-grained-treatment-to-synchronizations-in-gpu-to-cpu-translation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7389","title":{"rendered":"Fine-Grained Treatment to Synchronizations in GPU-to-CPU Translation"},"content":{"rendered":"<p>GPU-to-CPU translation may extend Graphics Processing Units (GPU) programs executions to multi-\/many-core CPUs, and hence enable cross-device task migration and promote whole-system synergy. This paper describes some of our findings in treatment to GPU synchronizations during the translation process. We show that careful dependence analysis may allow a fine-grained treatment to synchronizations and reveal redundant computation at the instruction-instance level. Based on thread-level dependence graphs, we present a method to enable such fine-grained treatment automatically. Experiments demonstrate that compared to existing translations, the new approach can yield speedup of a factor of integers.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>GPU-to-CPU translation may extend Graphics Processing Units (GPU) programs executions to multi-\/many-core CPUs, and hence enable cross-device task migration and promote whole-system synergy. This paper describes some of our findings in treatment to GPU synchronizations during the translation process. We show that careful dependence analysis may allow a fine-grained treatment to synchronizations and reveal redundant [&hellip;]<\/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":[215,955,1782,14,20],"class_list":["post-7389","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-code-generation","tag-compilers","tag-computer-science","tag-cuda","tag-nvidia"],"views":1787,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7389","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=7389"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7389\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7389"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7389"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7389"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}