{"id":7834,"date":"2012-07-02T23:49:27","date_gmt":"2012-07-02T20:49:27","guid":{"rendered":"http:\/\/hgpu.org\/?p=7834"},"modified":"2012-07-02T23:49:27","modified_gmt":"2012-07-02T20:49:27","slug":"halo-gathering-scalability-for-large-scale-multi-dimensional-sznajd-opinion-models-using-data-parallelism-with-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7834","title":{"rendered":"Halo Gathering Scalability for Large Scale Multi-dimensional Sznajd Opinion Models Using Data Parallelism with GPUs"},"content":{"rendered":"<p>The Sznajd model of opinion formation exhibits complex phase transitional and growth behaviour and can be studied with numerical simulations on a number of different network structures. Large system sizes and detailed statistical sampling of the model both require data-parallel computing to accelerate simulation performance. Data structures and computational performance issues are reported for simulations on single and multi-core processing devices. A discussion of optimal data structures for performance on Graphical Processing Units using NVIDIA&#8217;s Compute Unified Device Architecture (CUDA) is also given. System size and memory layout tradeoffs for different processing devices are also presented.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Sznajd model of opinion formation exhibits complex phase transitional and growth behaviour and can be studied with numerical simulations on a number of different network structures. Large system sizes and detailed statistical sampling of the model both require data-parallel computing to accelerate simulation performance. Data structures and computational performance issues are reported for simulations [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","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":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[11,89,3],"tags":[1782,14,263,285,20,974,535],"class_list":["post-7834","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-data-parallelism","tag-numerical-simulation","tag-nvidia","tag-nvidia-geforce-gtx-580","tag-phase-transition"],"views":2304,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7834","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=7834"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7834\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7834"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7834"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7834"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}