{"id":16803,"date":"2016-12-10T01:10:56","date_gmt":"2016-12-09T23:10:56","guid":{"rendered":"http:\/\/hgpu.org\/?p=16803"},"modified":"2016-12-10T01:10:56","modified_gmt":"2016-12-09T23:10:56","slug":"performance-evaluation-and-optimization-of-hpcg-benchmark-on-cpu-mic-platform","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=16803","title":{"rendered":"Performance Evaluation and Optimization of HPCG benchmark on CPU + MIC platform"},"content":{"rendered":"<p>High-performance conjugate gradient (HPCG) is the latest benchmark adopted by the TOP500 organization, and thus how to optimize the HPCG source code for different heterogeneous computing platforms to achieve a higher floating-point computation rate has already become a new hot issue in HPC field. In the paper, we used the CPU + MIC heterogeneous computing platforms, and successfully ported HPCG to the platform. Through the analysis of HPCG source code and optimization for CPU + MIC platforms, practical significance and the value of further research is put forward. Results of performing the benchmark indicate that the design of optimization methods is reasonable and has facilitated the speedup of HPCG benchmark.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>High-performance conjugate gradient (HPCG) is the latest benchmark adopted by the TOP500 organization, and thus how to optimize the HPCG source code for different heterogeneous computing platforms to achieve a higher floating-point computation rate has already become a new hot issue in HPC field. In the paper, we used the CPU + MIC heterogeneous computing [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"closed","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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,3],"tags":[451,1782,452,1483,252,67],"class_list":["post-16803","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-benchmarking","tag-computer-science","tag-heterogeneous-systems","tag-intel-xeon-phi","tag-openmp","tag-performance"],"views":1968,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16803","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=16803"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16803\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16803"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16803"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16803"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}