In 2012 a method to analyze N-body dark matter simulations using a tetrahedral tesselation of the three-dimensional dark matter manifold in six-dimensional phase space was introduced. This paper presents an accurate density computation approach for large N-body datasets, that is based on this technique and designed for massively parallel GPU-clusters. The densities are obtained by intersecting the tessellation with the cells of a spatially adaptive grid structure. We speed up this computational expensive part with an intersection algorithm, that is tailored to modern GPU architectures. We discuss different communication and dynamic load-balancing strategies and compare their weak and strong scaling efficiencies for several large N-body simulations.<\/p>\n","protected":false},"excerpt":{"rendered":"
In 2012 a method to analyze N-body dark matter simulations using a tetrahedral tesselation of the three-dimensional dark matter manifold in six-dimensional phase space was introduced. This paper presents an accurate density computation approach for large N-body datasets, that is based on this technique and designed for massively parallel GPU-clusters. The densities are obtained by […]<\/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":[96,89,3,12],"tags":[1794,98,14,97,258,20,176,1783,1740,866],"class_list":["post-16884","post","type-post","status-publish","format-standard","hentry","category-astrophysics","category-nvidia-cuda","category-paper","category-physics","tag-astrophysics","tag-computational-physics","tag-cuda","tag-instrumentation-and-methods-for-astrophysics","tag-n-body-simulation","tag-nvidia","tag-package","tag-physics","tag-tesla-k80","tag-tessellation"],"views":2603,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16884","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=16884"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/16884\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16884"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16884"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16884"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}