{"id":7464,"date":"2012-04-18T17:01:07","date_gmt":"2012-04-18T14:01:07","guid":{"rendered":"http:\/\/hgpu.org\/?p=7464"},"modified":"2012-04-18T17:01:07","modified_gmt":"2012-04-18T14:01:07","slug":"maximize-performance-on-gpus-using-the-rake-based-optimization-a-case-study","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7464","title":{"rendered":"Maximize Performance on GPUs Using the Rake-based Optimization: A Case Study"},"content":{"rendered":"<p>In this paper, we analyze the trade-offs encountered when minimizing the total execution time using the rake-based applications on GPUs. We use clustering data streams as a case study, and present a rake-based implementation for it, making it more efficient in terms of memory usage. In order to maximize performance for different problem sizes and architectures, we propose a model-based auto-tuning solution. Experimental results show that our fully optimized implementation can perform 2.1x and 1.4x faster than the native OpenCL implementation on NVIDIA GTX480 and AMD HD5870, respectively; it can also achieve 1.4x to 3.3x speedup relative to the original CUDA implementation solution on GTX480.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we analyze the trade-offs encountered when minimizing the total execution time using the rake-based applications on GPUs. We use clustering data streams as a case study, and present a rake-based implementation for it, making it more efficient in terms of memory usage. In order to maximize performance for different problem sizes and [&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":[11,89,90,3],"tags":[468,1782,14,20,379,1793],"class_list":["post-7464","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-clustering","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-opencl"],"views":1859,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7464","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=7464"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7464\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7464"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7464"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7464"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}