{"id":3712,"date":"2011-04-25T11:48:03","date_gmt":"2011-04-25T11:48:03","guid":{"rendered":"http:\/\/hgpu.org\/?p=3712"},"modified":"2011-04-25T11:48:03","modified_gmt":"2011-04-25T11:48:03","slug":"gpu-accelerated-fast-fem-deformation-simulation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=3712","title":{"rendered":"GPU accelerated fast FEM deformation simulation"},"content":{"rendered":"<p>In this paper we present a general FEM (finite element method) solution that enables fast dynamic deformation simulation on the newly available GPU (graphics processing unit) hardware with compute unified device architecture (CUDA) from NVIDIA. CUDA-enabled GPUs harness the power of 128 processors which allow data parallel computations. Compared to the previous GPGPU, it is significantly more flexible with a C language interface. We not only implement FEM deformation computation algorithms with CUDA but also analyze the performance in detail. Our test results indicate that the GPU with CUDA enables about 4 times speedup for FEM deformation computation on an Intel(R) Core 2 Quad 2.0 GHz machine with GeForce 8800 GTX.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we present a general FEM (finite element method) solution that enables fast dynamic deformation simulation on the newly available GPU (graphics processing unit) hardware with compute unified device architecture (CUDA) from NVIDIA. CUDA-enabled GPUs harness the power of 128 processors which allow data parallel computations. Compared to the previous GPGPU, it is [&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":[11,89,3],"tags":[1782,14,1037,212,20,183],"class_list":["post-3712","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-nvidia","tag-nvidia-geforce-8800-gtx"],"views":3040,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3712","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=3712"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/3712\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3712"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3712"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3712"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}