{"id":6530,"date":"2011-12-09T17:25:43","date_gmt":"2011-12-09T15:25:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=6530"},"modified":"2011-12-09T17:25:43","modified_gmt":"2011-12-09T15:25:43","slug":"gpu-implementations-of-scheduling-heuristics-for-heterogeneous-computing-environments","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6530","title":{"rendered":"GPU implementations of scheduling heuristics for heterogeneous computing environments"},"content":{"rendered":"<p>This work presents the application of parallel computing techniques using Graphic Processing Units to improve the efficiency of scheduling heuristics for heterogeneous computing systems. The experimental evaluation of the proposed methods demonstrates that a significant reduction on the computing times can be attained, allowing to tackle large scheduling scenarios in reasonable execution times.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This work presents the application of parallel computing techniques using Graphic Processing Units to improve the efficiency of scheduling heuristics for heterogeneous computing systems. The experimental evaluation of the proposed methods demonstrates that a significant reduction on the computing times can be attained, allowing to tackle large scheduling scenarios in reasonable execution times.<\/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":[1782,14,452,20,854,199],"class_list":["post-6530","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-task-scheduling","tag-tesla-c1060"],"views":1953,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6530","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=6530"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6530\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6530"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6530"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}