{"id":1277,"date":"2010-11-07T22:08:59","date_gmt":"2010-11-07T22:08:59","guid":{"rendered":"http:\/\/hgpu.org\/?p=1277"},"modified":"2010-11-07T22:08:59","modified_gmt":"2010-11-07T22:08:59","slug":"picongpu-a-fully-relativistic-particle-in-cell-code-for-a-gpu-cluster","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=1277","title":{"rendered":"PIConGPU: A Fully Relativistic Particle-in-Cell Code for a GPU Cluster"},"content":{"rendered":"<p>The particle-in-cell (PIC) algorithm is one of the most widely used algorithms in computational plasma physics. With the advent of graphical processing units (GPUs), large-scale plasma simulations on inexpensive GPU clusters are in reach. We present an implementation of a fully relativistic plasma PIC algorithm for GPUs based on the NVIDIA CUDA library. It supports a hybrid architecture consisting of single computation nodes interconnected in a standard cluster topology, with each node carrying one or more GPUs. The internode communication is realized using the message-passing interface. The simulation code PIConGPU presented in this paper is, to our knowledge, the first scalable GPU cluster implementation of the PIC algorithm in plasma physics.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The particle-in-cell (PIC) algorithm is one of the most widely used algorithms in computational plasma physics. With the advent of graphical processing units (GPUs), large-scale plasma simulations on inexpensive GPU clusters are in reach. We present an implementation of a fully relativistic plasma PIC algorithm for GPUs based on the NVIDIA CUDA library. It supports [&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":[89,90,3,12],"tags":[14,106,20,1793,299,1783,300,244],"class_list":["post-1277","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-opencl","category-paper","category-physics","tag-cuda","tag-gpu-cluster","tag-nvidia","tag-opencl","tag-particle-in-cell-methods","tag-physics","tag-plasma-physics","tag-tesla-s1070"],"views":3519,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1277","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=1277"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1277\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}