{"id":4000,"date":"2011-05-17T14:47:28","date_gmt":"2011-05-17T14:47:28","guid":{"rendered":"http:\/\/hgpu.org\/?p=4000"},"modified":"2011-05-17T14:47:28","modified_gmt":"2011-05-17T14:47:28","slug":"compile-time-gpu-memory-access-optimizations","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4000","title":{"rendered":"Compile-time GPU memory access optimizations"},"content":{"rendered":"<p>In the last three years, GPUs are more and more being used for general purpose applications instead of only for computer graphics. Programming these GPUs is a big challenge; in current GPUs the main bottleneck for many applications is not the computing power, but the memory access bandwidth. Two compile-time optimizations are presented in this paper to deal with the two most important memory access issues. To describe these optimizations, a new notation of the parallel execution of GPU programs is introduced. An implementation of the optimizations shows that performance improvements of up to 40 times are possible.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In the last three years, GPUs are more and more being used for general purpose applications instead of only for computer graphics. Programming these GPUs is a big challenge; in current GPUs the main bottleneck for many applications is not the computing power, but the memory access bandwidth. Two compile-time optimizations are presented in this [&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":[955,1782,14,20,226,298,70],"class_list":["post-4000","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-nvidia-geforce-8800-gt","tag-optimization","tag-programming-techniques"],"views":2004,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4000","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=4000"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4000\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4000"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4000"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4000"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}