{"id":14925,"date":"2015-11-11T23:02:16","date_gmt":"2015-11-11T21:02:16","guid":{"rendered":"http:\/\/hgpu.org\/?p=14925"},"modified":"2015-11-11T23:02:16","modified_gmt":"2015-11-11T21:02:16","slug":"climbing-mont-blanc-a-training-site-for-energy-efficient-programming-on-heterogeneous-multicore-processors","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=14925","title":{"rendered":"Climbing Mont Blanc &#8211; A Training Site for Energy Efficient Programming on Heterogeneous Multicore Processors"},"content":{"rendered":"<p>Climbing Mont Blanc (CMB) is an open online judge used for training in energy efficient programming of state-of-the-art heterogeneous multicores. It uses an Odroid-XU3 board from Hardkernel with an Exynos Octa processor and integrated power sensors. This processor is three-way heterogeneous containing 14 different cores of three different types. The board currently accepts C and C++ programs, with support for OpenCL v1.1, OpenMP 4.0 and Pthreads. Programs submitted using the graphical user interface are evaluated with respect to time, energy used, and energy-efficiency (EDP). A small and varied set of problems are available, and the system is currently in use in a medium sized course on parallel computing at NTNU. Other online programming judges exist, but we are not aware of any similar system that also reports energy-efficiency.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Climbing Mont Blanc (CMB) is an open online judge used for training in energy efficient programming of state-of-the-art heterogeneous multicores. It uses an Odroid-XU3 board from Hardkernel with an Exynos Octa processor and integrated power sensors. This processor is three-way heterogeneous containing 14 different cores of three different types. The board currently accepts C and [&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":[11,90,3],"tags":[1782,344,452,1793,252,573],"class_list":["post-14925","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-opencl","category-paper","tag-computer-science","tag-energy-efficient-computing","tag-heterogeneous-systems","tag-opencl","tag-openmp","tag-pthreads"],"views":2183,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14925","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=14925"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14925\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14925"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14925"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14925"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}