{"id":3481,"date":"2011-04-06T20:20:16","date_gmt":"2011-04-06T20:20:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=3481"},"modified":"2011-04-06T20:20:16","modified_gmt":"2011-04-06T20:20:16","slug":"heuristic-optimization-methods-for-improving-performance-of-recursive-general-purpose-applications-on-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3481","title":{"rendered":"Heuristic Optimization Methods for Improving Performance of Recursive General Purpose Applications on GPUs"},"content":{"rendered":"<p>Due to the demand of high definition graphics presentation in gaming and video market, graphics processing units (GPUs) have drastically increased their computational capacities. General-purpose computation on GPUs uses the fragment shader multicore of these processing units to concurrently process data streams. However, the I\/O overheads in recursive GPGPU applications have a negative impact in the performance of those systems. This paper proposes the remap method to improve the performance of general purpose recursive applications on GPUs, by decreasing the I\/O overheads imposed by the VRAM\/GPU interface. It is shown that significant performance improvements are achieved by applying the remap method to realistic recursive applications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Due to the demand of high definition graphics presentation in gaming and video market, graphics processing units (GPUs) have drastically increased their computational capacities. General-purpose computation on GPUs uses the fragment shader multicore of these processing units to concurrently process data streams. However, the I\/O overheads in recursive GPGPU applications have a negative impact in [&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":[11,3],"tags":[1782,298,67],"class_list":["post-3481","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-optimization","tag-performance"],"views":1697,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3481","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=3481"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3481\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3481"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3481"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}