Graphics Processing Units (GPUs) are widely used to unload the CPUs, liberate other resources of a given computer system, and provide an alternative to multiprocessor computers as a means of processing computationally expensive parallel tasks. The recent trend of utilizing GPUs in embedded systems necessitates the development of timing analysis techniques for finding the joint worst-case execution time for a group of GPU threads of the same parallel application, on a streaming multiprocessor. The state-of-the-art approaches for computing the exact maximum makespan of GPU threads running on a single streaming multiprocessor are intractable and even pessimistic approximations usually take a long time to complete. We therefore develop a technique for finding an estimate of the maximum makespan using metaheuristics. Its simplicity, flexibility and ability for massive parallelization, determine a potential of usage for soft real-time systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
Graphics Processing Units (GPUs) are widely used to unload the CPUs, liberate other resources of a given computer system, and provide an alternative to multiprocessor computers as a means of processing computationally expensive parallel tasks. The recent trend of utilizing GPUs in embedded systems necessitates the development of timing analysis techniques for finding the joint […]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_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},"jetpack_post_was_ever_published":false},"categories":[11,89,3],"tags":[1782,14,748,20,1306,193,532],"class_list":["post-9210","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-metaheuristics","tag-nvidia","tag-nvidia-geforce-gtx-680","tag-ptx","tag-timing-analysis"],"views":2603,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9210","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=9210"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/9210\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9210"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9210"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9210"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}