{"id":6636,"date":"2011-12-19T17:13:10","date_gmt":"2011-12-19T15:13:10","guid":{"rendered":"http:\/\/hgpu.org\/?p=6636"},"modified":"2011-12-19T17:13:10","modified_gmt":"2011-12-19T15:13:10","slug":"optimization-of-mapped-functions-sequences-using-fusions-on-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=6636","title":{"rendered":"Optimization of mapped functions sequences using fusions on GPU"},"content":{"rendered":"<p>When implementing a function mapping on the contemporary GPU, several contradictory performance factors have to be balanced. Previously a decomposition-fusion scheme was devised to guide such an implementation and this work is here further elaborated. To ease this process, an automatic source-to-source compiler is presented, while the main subject of this thesis are the core algorithms for generation, pruning and search in the state-space of possible implementations of the mapped function. The performance of the generated implementation is evaluated together with the overall complexity of the optimization process.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When implementing a function mapping on the contemporary GPU, several contradictory performance factors have to be balanced. Previously a decomposition-fusion scheme was devised to guide such an implementation and this work is here further elaborated. To ease this process, an automatic source-to-source compiler is presented, while the main subject of this thesis are the core [&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":[36,11,89,3],"tags":[1787,1782,14,242,20,379,298,390],"class_list":["post-6636","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-mpi","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-optimization","tag-thesis"],"views":1772,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6636","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=6636"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6636\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6636"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6636"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6636"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}