{"id":5735,"date":"2011-09-30T23:03:26","date_gmt":"2011-09-30T20:03:26","guid":{"rendered":"http:\/\/hgpu.org\/?p=5735"},"modified":"2011-09-30T23:03:26","modified_gmt":"2011-09-30T20:03:26","slug":"fatsea-an-architectural-simulator-for-general-purpose-computing-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5735","title":{"rendered":"FATSEA-An Architectural Simulator for General Purpose Computing on GPUs"},"content":{"rendered":"<p>We present FATSEA, a functional and performance evaluation simulator written in C++ to handle kernels written in the CUDA programming language aimed for GPGPU computing. FATSEA takes a Parallel Thread eXecution (PTX ) code as input, which is a device independent code format generated by the Nvidia CUDA compiler, to validate results and estimate performance on Nvidia platforms. This paper shows results on a G80-based architecture for a set of well-known kernels to illustrate the usefulness of our framework while performing a preliminary validation for the tool.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present FATSEA, a functional and performance evaluation simulator written in C++ to handle kernels written in the CUDA programming language aimed for GPGPU computing. FATSEA takes a Parallel Thread eXecution (PTX ) code as input, which is a device independent code format generated by the Nvidia CUDA compiler, to validate results and estimate performance [&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":[11,89,3],"tags":[955,1782,14,20,67,193,202],"class_list":["post-5735","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-compilers","tag-computer-science","tag-cuda","tag-nvidia","tag-performance","tag-ptx","tag-tesla-c870"],"views":1834,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5735","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=5735"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5735\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5735"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5735"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5735"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}