N-body simulations are computation-intensive ap-plications that calculate the motion of a large number of bodies under pair-wise forces. Although different versions of n-body codes have been widely used in many scientific fields, the perfor-mance and energy efficiency of various n-body codes have not been comprehensively studied, especially when they are running on newly released multi-core CPUs and GPUs (e.g., Tesla K20). In this paper, we evaluate the performance and energy efficiency of five parallel n-body implementations on two different multi-core CPU systems and on two different types of GPUs. Our ex-perimental results show that up to 71% of the energy can be saved by using all cores of a Xeon E5620 CPU instead of only one. We find hyper-threading to be able to further reduce the energy usage and runtime, but not by as much as adding more cores does. Finally, our experiments illustrate that GPU-based acceleration using a Tesla K20c can boost the performance and energy efficiency by orders of magnitude.<\/p>\n","protected":false},"excerpt":{"rendered":"
N-body simulations are computation-intensive ap-plications that calculate the motion of a large number of bodies under pair-wise forces. Although different versions of n-body codes have been widely used in many scientific fields, the perfor-mance and energy efficiency of various n-body codes have not been comprehensively studied, especially when they are running on newly released multi-core […]<\/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":[1782,14,258,20,379,67],"class_list":["post-10969","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-n-body-simulation","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-performance"],"views":2122,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10969","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=10969"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10969\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10969"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10969"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10969"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}