{"id":7092,"date":"2012-02-06T14:12:02","date_gmt":"2012-02-06T12:12:02","guid":{"rendered":"http:\/\/hgpu.org\/?p=7092"},"modified":"2012-02-06T14:12:02","modified_gmt":"2012-02-06T12:12:02","slug":"gpu-based-swendsen-wang-multi-cluster-algorithm-for-the-simulation-of-two-dimensional-classical-spin-systems","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7092","title":{"rendered":"GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional classical spin systems"},"content":{"rendered":"<p>We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling algorithms recently proposed with CUDA for the assignment of the cluster in the Swendsen-Wang algorithm. Starting with the q-state Potts model, we extend our implementation to the system of vector spins, the q-state clock model, with the idea of embedded cluster. We test the performance, and the calculation time on GTX580 is obtained as 2.51 nano sec per a spin flip for the q=2 Potts model (Ising model) and 2.42 nano sec per a spin flip for the q=6 clock model with the linear size L=4096 at the critical temperature, respectively. The computational speed for the q=2 Potts model on GTX580 is 12.4 times as fast as the calculation speed on a current CPU core. That for the q=6 clock model on GTX580 is 35.6 times as fast as the calculation speed on a current CPU core.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present the GPU calculation with the common unified device architecture (CUDA) for the Swendsen-Wang multi-cluster algorithm of two-dimensional classical spin systems. We adjust the two connected component labeling algorithms recently proposed with CUDA for the assignment of the cluster in the Swendsen-Wang algorithm. Starting with the q-state Potts model, we extend our implementation to [&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":[36,89,3,12],"tags":[1787,98,14,71,72,20,251,974,1783,198,103],"class_list":["post-7092","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-nvidia-cuda","category-paper","category-physics","tag-algorithms","tag-computational-physics","tag-cuda","tag-ising-model","tag-monte-carlo-simulation","tag-nvidia","tag-nvidia-geforce-gtx-285","tag-nvidia-geforce-gtx-580","tag-physics","tag-spin-systems","tag-statistical-mechanics"],"views":2719,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7092","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=7092"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7092\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7092"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7092"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7092"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}