Graphics processing units (GPUs) are becoming increasingly important in today’s platforms as their increased generality allows for them to be used as powerful co-processors. However, unlike standard CPUs, GPUs are treated as I\/O devices and require the use of interrupts to facilitate communication with the CPU. Interrupts cause delays in the execution of real-time tasks, challenges in realtime operating system implementation, and difficulties for formal analysis. We examine methods for designing proper real-time interrupt handling in multiprocessor systems and present our solution, klitirqd, an addition to LITMUS RT, to address the challenges caused by interrupts in real-time systems. klitirqd is a flexible solution that improves upon prior approaches by supporting non-partitioned multiprocessor scheduling algorithms while respecting the single-threaded sporadic task model and also supporting asynchronous I\/O. We use klitirqd to realize real-time GPU interrupt handling and overcome significant technical challenges of altering the interrupt processes of a closed-source GPU driver. This technique can be generalized to potentially support any closed-source device driver.<\/p>\n","protected":false},"excerpt":{"rendered":"
Graphics processing units (GPUs) are becoming increasingly important in today’s platforms as their increased generality allows for them to be used as powerful co-processors. However, unlike standard CPUs, GPUs are treated as I\/O devices and require the use of interrupts to facilitate communication with the CPU. Interrupts cause delays in the execution of real-time tasks, […]<\/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":[36,11,89,3],"tags":[1787,1782,14,20,953,852],"class_list":["post-6489","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-470","tag-operating-systems"],"views":2379,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6489","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=6489"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6489\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6489"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6489"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6489"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}