{"id":15590,"date":"2016-03-20T01:30:11","date_gmt":"2016-03-19T23:30:11","guid":{"rendered":"http:\/\/hgpu.org\/?p=15590"},"modified":"2016-03-20T01:30:11","modified_gmt":"2016-03-19T23:30:11","slug":"acceleration-of-ensemble-machine-learning-methods-using-many-core-devices","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15590","title":{"rendered":"Acceleration of ensemble machine learning methods using many-core devices"},"content":{"rendered":"<p>We present a case study into the acceleration of ensemble machine learning methods using many-core devices in collaboration with Toshiba Medical Visualisation Systems Europe (TMVSE). The adoption of GPUs to execute a key algorithm in the classification of medical image data was shown to significantly reduce overall processing time. Using a representative dataset and pre-trained decision trees as input we will demonstrate how the decision forest classification method can be mapped onto the GPU data processing model. It was found that a GPU-based version of the decision forest method resulted in over 138 times speed-up over a single-threaded CPU implementation with further improvements possible. The same GPU-based software was then directly applied to a suitably formed dataset to benefit supervised learning techniques applied in High Energy Physics (HEP) with similar improvements in performance.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a case study into the acceleration of ensemble machine learning methods using many-core devices in collaboration with Toshiba Medical Visualisation Systems Europe (TMVSE). The adoption of GPUs to execute a key algorithm in the classification of medical image data was shown to significantly reduce overall processing time. Using a representative dataset and pre-trained [&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":[11,89,3,12],"tags":[1782,14,1025,20,1783,1543],"class_list":["post-15590","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-physics","tag-computer-science","tag-cuda","tag-machine-learning","tag-nvidia","tag-physics","tag-tesla-k40"],"views":2268,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15590","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=15590"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15590\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15590"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15590"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15590"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}