{"id":2306,"date":"2011-01-04T13:21:30","date_gmt":"2011-01-04T13:21:30","guid":{"rendered":"http:\/\/hgpu.org\/?p=2306"},"modified":"2011-01-04T13:21:30","modified_gmt":"2011-01-04T13:21:30","slug":"gpu-based-conjugate-gradient-solver-for-lattice-qcd-with-domain-wall-fermions","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2306","title":{"rendered":"GPU-Based Conjugate Gradient Solver for Lattice QCD with Domain-Wall Fermions"},"content":{"rendered":"<p>We present the first GPU-based conjugate gradient (CG) solver for lattice QCD with domain-wall fermions (DWF). It is well-known that CG is the most time-consuming part in the Hybrid Monte Carlo simulation of unquenched lattice QCD, which becomes even more computational demanding for lattice QCD with exact chiral symmetry. We have designed a CG solver for the general 5-dimensional DWF operator on NVIDIA CUDA architecture with mixed-precision, using the defect correction as well as the reliable updates algorithms. We optimize our computation by even-odd preconditioning in the 4D space-time lattice, plus several innovative techniques for CUDA kernels. For NVIDIA GeForce GTX 285\/480, our CG solver attains 180\/233 Gflops (sustained).<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the first GPU-based conjugate gradient (CG) solver for lattice QCD with domain-wall fermions (DWF). It is well-known that CG is the most time-consuming part in the Hybrid Monte Carlo simulation of unquenched lattice QCD, which becomes even more computational demanding for lattice QCD with exact chiral symmetry. We have designed a CG solver [&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":[89,3,12],"tags":[580,14,110,72,20,251,379,1783,335],"class_list":["post-2306","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-conjugate-gradient-solver","tag-cuda","tag-high-energy-physics-lattice","tag-monte-carlo-simulation","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-nvidia-geforce-gtx-480","tag-physics","tag-qcd"],"views":2125,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2306","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=2306"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2306\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2306"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2306"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2306"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}