{"id":11077,"date":"2013-12-11T23:49:59","date_gmt":"2013-12-11T21:49:59","guid":{"rendered":"http:\/\/hgpu.org\/?p=11077"},"modified":"2013-12-11T23:49:59","modified_gmt":"2013-12-11T21:49:59","slug":"a-new-software-based-gpu-framework","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11077","title":{"rendered":"A New Software Based GPU Framework"},"content":{"rendered":"<p>A software based GPU design, where most of the 3D pipeline is executed in software on shaders, with minimal support from custom hardware blocks, provides three benefits, it: (1) simplifies the GPU design, (2) turns 3D graphics into a general purpose application, and (3) opens the door for applying compiler optimization to the whole 3D pipeline. In this thesis we design a framework and a full software stack to support further research in the field. LLVM IR is used as a flexible shader IR, and all fixed-function hardware blocks are translated into it. A sort-middle, tile-based, architecture is used for the 3D pipeline and trace-file based methodology is applied to make the system more modular. Further, we implement a GPU model and use it to perform an architectural exploration of the proposed software based GPU system design space.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A software based GPU design, where most of the 3D pipeline is executed in software on shaders, with minimal support from custom hardware blocks, provides three benefits, it: (1) simplifies the GPU design, (2) turns 3D graphics into a general purpose application, and (3) opens the door for applying compiler optimization to the whole 3D [&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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,20,379,390],"class_list":["post-11077","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-thesis"],"views":2556,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11077","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=11077"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11077\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11077"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11077"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11077"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}