{"id":2740,"date":"2011-02-06T12:38:52","date_gmt":"2011-02-06T12:38:52","guid":{"rendered":"http:\/\/hgpu.org\/?p=2740"},"modified":"2011-02-06T12:38:52","modified_gmt":"2011-02-06T12:38:52","slug":"xmt-gpu-a-pram-architecture-for-graphics-computation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2740","title":{"rendered":"XMT-GPU: A PRAM Architecture for Graphics Computation"},"content":{"rendered":"<p>The shading processors in graphics hardware are becoming increasingly general-purpose. We test, through simulation and benchmarking, the potential performance impact of replacing these processors with a fully general-purpose parallel processor, without the fixed-function graphics hardware legacy of current graphics processing units (GPUs). The representative general-purpose processor we test against is XMT (for explicit multi-threading), a PRAM-like single-chip parallel architecture. Performance is compared for two characteristic shaders running in a fragment-limited GPU benchmark harness and on a cycle-accurate XMT simulator. The general-purpose processor is found to be significantly faster at a compute-only shader, but slower on a memory bound texture shader. Finally we analyze the design tradeoffs that would allow combining the best of both worlds: (i) a competitive XMT texture shader, with (ii) a general-purpose easy-to-program XMT many-core approach that scales up or down to the amount of parallelism provided by the application and is even compatible with serial code.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The shading processors in graphics hardware are becoming increasingly general-purpose. We test, through simulation and benchmarking, the potential performance impact of replacing these processors with a fully general-purpose parallel processor, without the fixed-function graphics hardware legacy of current graphics processing units (GPUs). The representative general-purpose processor we test against is XMT (for explicit multi-threading), a [&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":[36,11,3],"tags":[1787,7,979,1782,20,191,183,182,70],"class_list":["post-2740","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-ati","tag-ati-radeon-x700-pro","tag-computer-science","tag-nvidia","tag-nvidia-geforce-7900-gtx","tag-nvidia-geforce-8800-gtx","tag-opengl","tag-programming-techniques"],"views":2235,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2740","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=2740"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2740\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2740"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2740"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2740"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}