{"id":8828,"date":"2013-01-23T23:54:38","date_gmt":"2013-01-23T21:54:38","guid":{"rendered":"http:\/\/hgpu.org\/?p=8828"},"modified":"2013-01-23T23:54:38","modified_gmt":"2013-01-23T21:54:38","slug":"implementing-open-source-cuda-runtime","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=8828","title":{"rendered":"Implementing Open-Source CUDA Runtime"},"content":{"rendered":"<p>Graphics processing units (GPUs) are the state of the art embracing the concept of many-core technology. Their significant advantage in performance and performanceper-watt compared to traditional microprocessors has facilitated development of GPUs in many compute applications. However, GPUs are often treated as &quot;black-box&quot; devices due to proprietary strategies of hardware vendors. One of the greatest challenges of this research domain is the in-depth understanding of GPU architectures and runtime mechanisms so that the systems research community can tackle fundamental problems of GPUs. In this paper, we present an open-source implementation of CUDA runtime, which is the most widely-recognized programming framework for GPUs, as well as a documentation of &quot;how GPUs work&quot; investigated by our reverse engineering work. Our implementation is based on Linux and is targeted at NVIDIA GPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Graphics processing units (GPUs) are the state of the art embracing the concept of many-core technology. Their significant advantage in performance and performanceper-watt compared to traditional microprocessors has facilitated development of GPUs in many compute applications. However, GPUs are often treated as &quot;black-box&quot; devices due to proprietary strategies of hardware vendors. One of the greatest [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":[1782,14,20,852,176],"class_list":["post-8828","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-operating-systems","tag-package"],"views":4004,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8828","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=8828"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/8828\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8828"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8828"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8828"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}