{"id":11347,"date":"2014-02-02T21:28:34","date_gmt":"2014-02-02T19:28:34","guid":{"rendered":"http:\/\/hgpu.org\/?p=11347"},"modified":"2014-02-02T21:28:34","modified_gmt":"2014-02-02T19:28:34","slug":"task-migration-of-dsp-application-specified-with-a-dfg-and-implemented-with-the-bsp-computing-model-on-a-cpu-gpu-cluster","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=11347","title":{"rendered":"Task migration of DSP application specified with a DFG and implemented with the BSP computing model on a CPU-GPU cluster"},"content":{"rendered":"<p>Nowadays computer applications are becoming heavier and require, at the same time, real-time results. The Heterogeneous clusters with their computing power represent a good solution to this request. However, it is possible that during the execution, a computing element of the cluster becomes defaulting, needs maintenance, or that the load needs to be re-balanced. In this paper, we propose a migration strategy for relocating the execution of a task to another computing element. In particular, we are interested in remap nodes of Data Flow Graph (DFG), representing Digital Signal Processing (DSP) application, onto heterogeneous (CPU-GPU) clusters while keeping up the flow of data and minimizing the temporal perturbation. For our approach, we give a lower bound for the flow of data after the migration and, validate it by the real-time construction of visual saliency map from video input.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Nowadays computer applications are becoming heavier and require, at the same time, real-time results. The Heterogeneous clusters with their computing power represent a good solution to this request. However, it is possible that during the execution, a computing element of the cluster becomes defaulting, needs maintenance, or that the load needs to be re-balanced. In [&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":[11,89,3,41],"tags":[1782,14,809,106,452,20,251,1464,1789],"class_list":["post-11347","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","category-signal-processing","tag-computer-science","tag-cuda","tag-dsp","tag-gpu-cluster","tag-heterogeneous-systems","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-nvidia-quadro-4000","tag-signal-processing"],"views":2618,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11347","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=11347"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11347\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11347"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11347"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11347"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}