{"id":2845,"date":"2011-02-14T12:02:57","date_gmt":"2011-02-14T12:02:57","guid":{"rendered":"http:\/\/hgpu.org\/?p=2845"},"modified":"2011-02-14T12:02:57","modified_gmt":"2011-02-14T12:02:57","slug":"numerical-simulation-of-the-complex-ginzburg-landau-equation-on-gpus-with-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=2845","title":{"rendered":"Numerical Simulation of the Complex Ginzburg-Landau Equation on GPUs with CUDA"},"content":{"rendered":"<p>The Time Dependent Ginzburg Landau(TDGL) equation models a complex scalar field and is used to study a variety of different physical systems and exhibits phase transitional behaviours that necessitate study using numerical simulation methods. We employ fast data-parallel simulation algorithms on Graphical Processing Units (GPUs) and report on performance data and stability tradeoffs from using various implementations of both 32-bit and 64-bit complex numbers. Using NVIDIA&#8217;s Compute Unified Device Architecture (CUDA) programming language running on a GTX480 GPU, we are able to simulate the TDGL with relatively large simulation system sizes of 256^3 cells and we discuss the relative computational tradeoffs between numerical accuracy and stability using different methods as well as different data precisions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Time Dependent Ginzburg Landau(TDGL) equation models a complex scalar field and is used to study a variety of different physical systems and exhibits phase transitional behaviours that necessitate study using numerical simulation methods. We employ fast data-parallel simulation algorithms on Graphical Processing Units (GPUs) and report on performance data and stability tradeoffs from using [&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":[14,327,20,379,550,551,1783,505,134],"class_list":["post-2845","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-paper","category-physics","tag-cuda","tag-finite-difference","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-partial-differential-equations","tag-pdes","tag-physics","tag-thermodynamics","tag-visualization"],"views":2501,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2845","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=2845"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2845\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2845"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2845"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2845"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}