{"id":7519,"date":"2012-05-02T17:43:33","date_gmt":"2012-05-02T14:43:33","guid":{"rendered":"http:\/\/hgpu.org\/?p=7519"},"modified":"2012-05-02T17:43:33","modified_gmt":"2012-05-02T14:43:33","slug":"automatic-numa-characterization-using-cbench","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7519","title":{"rendered":"Automatic NUMA Characterization using Cbench"},"content":{"rendered":"<p>Clusters of seemingly homogeneous compute nodes are increasingly heterogeneous within each node due to replication and distribution of node-level subsystems. This intra-node heterogeneity can adversely affect program execution performance by inflicting additional data-access costs when accessing non-local data. In this work-in-progress paper, we present extensions to the Cbench Scalable Testing Framework for analyzing main memory and PCIe data-access performance in modern NUMA architectures. The information provided by this tool will be of use for task scheduling, performance modeling, and evaluation of NUMA systems.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Clusters of seemingly homogeneous compute nodes are increasingly heterogeneous within each node due to replication and distribution of node-level subsystems. This intra-node heterogeneity can adversely affect program execution performance by inflicting additional data-access costs when accessing non-local data. In this work-in-progress paper, we present extensions to the Cbench Scalable Testing Framework for analyzing main memory [&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":[451,1782,14,452,20,1793,176,854,378],"class_list":["post-7519","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-benchmarking","tag-computer-science","tag-cuda","tag-heterogeneous-systems","tag-nvidia","tag-opencl","tag-package","tag-task-scheduling","tag-tesla-c2050"],"views":2504,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7519","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=7519"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7519\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7519"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7519"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7519"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}