{"id":10414,"date":"2013-08-28T23:29:52","date_gmt":"2013-08-28T20:29:52","guid":{"rendered":"http:\/\/hgpu.org\/?p=10414"},"modified":"2013-08-28T23:29:52","modified_gmt":"2013-08-28T20:29:52","slug":"dynamic-load-balancing-on-massively-parallel-computer-architectures","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10414","title":{"rendered":"Dynamic Load Balancing on Massively Parallel Computer Architectures"},"content":{"rendered":"<p>This thesis reports on using dynamic load balancing methods on massively parallel computers in the context of multi-threaded computations. In particular we investigate the applicability of a randomized work stealing algorithm to ray tracing and breadth-first search as representatives of real-world applications with dynamic work creation. For our considerations we made use of current massively parallel hardware accelerators: Nvidia Tesla M2090, and Intel Xeon Phi. For both of the two we demonstrate the suitability of the work stealing scheme for the said real-world applications. Also the necessity of dynamic load balancing for irregular computations on such hardware is illustrated.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This thesis reports on using dynamic load balancing methods on massively parallel computers in the context of multi-threaded computations. In particular we investigate the applicability of a randomized work stealing algorithm to ray tracing and breadth-first search as representatives of real-world applications with dynamic work creation. For our considerations we made use of current massively [&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],"tags":[1782,14,1388,20,67,181,1241,390],"class_list":["post-10414","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-intel-phi","tag-nvidia","tag-performance","tag-raytracing","tag-tesla-m2090","tag-thesis"],"views":2160,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10414","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=10414"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10414\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10414"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10414"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10414"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}