{"id":2614,"date":"2011-01-25T10:43:06","date_gmt":"2011-01-25T10:43:06","guid":{"rendered":"http:\/\/hgpu.org\/?p=2614"},"modified":"2011-01-25T10:43:06","modified_gmt":"2011-01-25T10:43:06","slug":"fast-schedulability-analysis-using-commodity-graphics-hardware","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2614","title":{"rendered":"Fast Schedulability Analysis Using Commodity Graphics Hardware"},"content":{"rendered":"<p>In this paper we explore the possibility of using commodity graphics processing units (GPUs) to speedup standard schedulability analysis algorithms. Our long-term goal is to exploit GPUs to accelerate common electronic design automation algorithms, most of which tend to be computationally expensive. Our main contribution in this paper is a reformulation of a standard demand bound criteria-based schedulability analysis algorithm as a streaming algorithm expressed in terms of computer graphics primitives. This allows the algorithm to be efficiently implemented on a GPU, thereby resulting in very attractive speedups.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we explore the possibility of using commodity graphics processing units (GPUs) to speedup standard schedulability analysis algorithms. Our long-term goal is to exploit GPUs to accelerate common electronic design automation algorithms, most of which tend to be computationally expensive. Our main contribution in this paper is a reformulation of a standard demand [&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":[11,3],"tags":[444,1782,20,183,182,298],"class_list":["post-2614","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-cg","tag-computer-science","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-opengl","tag-optimization"],"views":1749,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2614","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=2614"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2614\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2614"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2614"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2614"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}