This work presents the novel parallel evolutionary algorithm (EA) for task scheduling in distributed heterogeneous computing and grid environments, NP-hard problems with capital relevance in distributed computing. Parallelization of the biologically inspired heuristics is hierarchically designed and integrates with the two traditional parallel models (master-slave models and island models). The method has been specifically implemented on the newly developed supercomputer platform of hybrid multi-core CPU+GPU using C-CUDA for solving large-sized realistic instances. Experiments are performed on both well-known problem instances and large instances that model medium-sized grid environments. The comparative study shows that the proposed parallel approach is able to achieve high solving efficacy, outperforming previous results reported in the related literature, and also showing a good scalability behavior when facing high dimension problem instances.<\/p>\n","protected":false},"excerpt":{"rendered":"
This work presents the novel parallel evolutionary algorithm (EA) for task scheduling in distributed heterogeneous computing and grid environments, NP-hard problems with capital relevance in distributed computing. Parallelization of the biologically inspired heuristics is hierarchically designed and integrates with the two traditional parallel models (master-slave models and island models). The method has been specifically implemented […]<\/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":[1782,14,510,452,20,854],"class_list":["post-11956","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-distributed-computing","tag-heterogeneous-systems","tag-nvidia","tag-task-scheduling"],"views":2092,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11956","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=11956"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/11956\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=11956"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=11956"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=11956"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}