{"id":10549,"date":"2013-09-18T23:29:00","date_gmt":"2013-09-18T20:29:00","guid":{"rendered":"http:\/\/hgpu.org\/?p=10549"},"modified":"2013-09-18T23:29:00","modified_gmt":"2013-09-18T20:29:00","slug":"a-gpu-based-parallel-procedure-for-nonlinear-analysis-of-complex-structures-using-a-coupled-femdem-approach","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10549","title":{"rendered":"A GPU-based Parallel Procedure for Nonlinear Analysis of Complex Structures Using a Coupled FEM\/DEM Approach"},"content":{"rendered":"<p>This study reports the GPU parallelization of complex three-dimensional software for nonlinear analysis of concrete structures. It focuses on coupled thermo-mechanical analysis of complex structures. A coupled FEM\/DEM approach (CDEM) is given from a fundamental theoretical viewpoint. As the modeling of a large structure by means of FEM\/DEM may lead to prohibitive computation times, a parallelization strategy is required. With the substantial development of computer science, a GPU-based parallel procedure is implemented. A comparative study between the GPU and CPU computation results is presented, and the runtimes and speedups are analyzed. The results show that dramatic performance improvements are gained from GPU parallelization.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This study reports the GPU parallelization of complex three-dimensional software for nonlinear analysis of concrete structures. It focuses on coupled thermo-mechanical analysis of complex structures. A coupled FEM\/DEM approach (CDEM) is given from a fundamental theoretical viewpoint. As the modeling of a large structure by means of FEM\/DEM may lead to prohibitive computation times, a [&hellip;]<\/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":[1782,14,1037,212,211,20,974,1394,1306],"class_list":["post-10549","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-fem","tag-finite-element-method","tag-nonlinear-analysis","tag-nvidia","tag-nvidia-geforce-gtx-580","tag-nvidia-geforce-gtx-670","tag-nvidia-geforce-gtx-680"],"views":2219,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10549","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=10549"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10549\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10549"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10549"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10549"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}