{"id":2903,"date":"2011-02-19T14:59:43","date_gmt":"2011-02-19T14:59:43","guid":{"rendered":"http:\/\/hgpu.org\/?p=2903"},"modified":"2011-02-19T14:59:43","modified_gmt":"2011-02-19T14:59:43","slug":"a-generic-approach-for-developing-highly-scalable-particle-mesh-codes-for-gpus","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2903","title":{"rendered":"A Generic Approach for Developing Highly Scalable Particle-Mesh Codes for GPUs"},"content":{"rendered":"<p>We present a general framework for GPU-based low-latency data transfer schemes that can be used for a variety of particle-mesh algorithms [8]. This framework allows to hide the latency of the data transfer between GPU-accelerated computing nodes by interleaving it with the kernel execution on the GPU. We discuss as an example the fully relativistic particle-in-cell (PiC) code PIConGPU [5] currently used to simulate particle acceleration by extremely short high-energy laser pulses. The PiC algorithm is a versatile algorithm used frequently in plasma physics-especially for large-scale simulations of fusion plasmas [13]-, in astrophysics [7], [9], or for the simulation of particle accelerators [11]. A special Cell processor version is used as a benchmark code for the Roadrunner system at Los Alamos National Lab [4].<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a general framework for GPU-based low-latency data transfer schemes that can be used for a variety of particle-mesh algorithms [8]. This framework allows to hide the latency of the data transfer between GPU-accelerated computing nodes by interleaving it with the kernel execution on the GPU. We discuss as an example the fully relativistic [&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":[11,89,90,3],"tags":[454,1782,14,20,1793,299,300,378,244],"class_list":["post-2903","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-opencl","category-paper","tag-ati-radeon-hd-5770","tag-computer-science","tag-cuda","tag-nvidia","tag-opencl","tag-particle-in-cell-methods","tag-plasma-physics","tag-tesla-c2050","tag-tesla-s1070"],"views":2249,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2903","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=2903"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2903\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2903"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2903"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2903"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}