{"id":1564,"date":"2010-11-21T21:53:03","date_gmt":"2010-11-21T21:53:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=1564"},"modified":"2010-11-21T21:53:03","modified_gmt":"2010-11-21T21:53:03","slug":"a-high-performance-agent-based-modelling-framework-on-graphics-card-hardware-with-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1564","title":{"rendered":"A high performance agent based modelling framework on graphics card hardware with CUDA"},"content":{"rendered":"<p>We present an efficient implementation of a high performance parallel framework for Agent Based Modelling (ABM), exploiting the parallel architecture of the Graphics Processing Unit (GPU). It provides a mapping between formal agent specifications, with C based scripting, and optimised NVIDIA Compute Unified Device Architecture (CUDA) code. The mapping of agent data structures and agent communication is described, and our work is evaluated through a number of simple interacting agent examples. In contrast with an alternative, single machine CPU implementation, a speedup of up to 250 times is reported.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present an efficient implementation of a high performance parallel framework for Agent Based Modelling (ABM), exploiting the parallel architecture of the Graphics Processing Unit (GPU). It provides a mapping between formal agent specifications, with C based scripting, and optimised NVIDIA Compute Unified Device Architecture (CUDA) code. The mapping of agent data structures and agent [&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":[117,1782,14,20,311],"class_list":["post-1564","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-artificial-intelligence","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-9800-gx2"],"views":1896,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1564","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=1564"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1564\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1564"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1564"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1564"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}