{"id":5311,"date":"2011-08-27T23:19:12","date_gmt":"2011-08-27T20:19:12","guid":{"rendered":"http:\/\/hgpu.org\/?p=5311"},"modified":"2011-08-27T23:19:12","modified_gmt":"2011-08-27T20:19:12","slug":"switching-to-high-gear-opportunities-for-grand-scale-real-time-parallel-simulations","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=5311","title":{"rendered":"Switching to High Gear: Opportunities for Grand-Scale Real-Time Parallel Simulations"},"content":{"rendered":"<p>The recent emergence of dramatically large computational power, spanning desktops with multi-core processors and multiple graphics cards to supercomputers with 105 processor cores, has suddenly resulted in simulation-based solutions trailing behind in the ability to fully tap the new computational capacity. Here, we motivate the need for switching the parallel simulation research to a higher gear to exploit the new, immense levels of computational power. The potential for grand-scale real-time solutions is illustrated using preliminary results from prototypes in four example application areas: (a) state- or regional-scale vehicular mobility modeling, (b) very large-scale epidemic modeling, (c) modeling the propagation of wireless network signals in very large, cluttered terrains, and, (d) country- or world-scale social behavioral modeling. We believe the stage is perfectly poised for the parallel\/distributed simulation community to envision and formulate similar grand-scale, real-time simulation-based solutions in many application areas.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The recent emergence of dramatically large computational power, spanning desktops with multi-core processors and multiple graphics cards to supercomputers with 105 processor cores, has suddenly resulted in simulation-based solutions trailing behind in the ability to fully tap the new computational capacity. Here, we motivate the need for switching the parallel simulation research to a higher [&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":[1782,14,242,20,67,31],"class_list":["post-5311","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-mpi","tag-nvidia","tag-performance","tag-review"],"views":1989,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5311","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=5311"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/5311\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=5311"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=5311"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=5311"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}