{"id":15959,"date":"2016-06-07T00:20:29","date_gmt":"2016-06-06T21:20:29","guid":{"rendered":"http:\/\/hgpu.org\/?p=15959"},"modified":"2016-06-07T00:20:29","modified_gmt":"2016-06-06T21:20:29","slug":"bit-vectorized-gpu-implementation-of-a-stochastic-cellular-automaton-model-for-surface-growth","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15959","title":{"rendered":"Bit-Vectorized GPU Implementation of a Stochastic Cellular Automaton Model for Surface Growth"},"content":{"rendered":"<p>Stochastic surface growth models aid in studying properties of universality classes like the Kardar&#8211;Paris&#8211;Zhang class. High precision results obtained from large scale computational studies can be transferred to many physical systems. Many properties, such as roughening and some two-time functions can be studied using stochastic cellular automaton (SCA) variants of stochastic models. Here we present a highly efficient SCA implementation of a surface growth model capable of simulating billions of lattice sites on a single GPU. We also provide insight into cases requiring arbitrary random probabilities which are not accessible through bit-vectorization.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Stochastic surface growth models aid in studying properties of universality classes like the Kardar&#8211;Paris&#8211;Zhang class. High precision results obtained from large scale computational studies can be transferred to many physical systems. Many properties, such as roughening and some two-time functions can be studied using stochastic cellular automaton (SCA) variants of stochastic models. Here we present [&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":[98,1782,14,166,20,1767,1895,103,1740],"class_list":["post-15959","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computational-physics","tag-computer-science","tag-cuda","tag-materials-science","tag-nvidia","tag-nvidia-geforce-gtx-titan-x","tag-nvidia-geforce-titan-black","tag-statistical-mechanics","tag-tesla-k80"],"views":2062,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15959","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=15959"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15959\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15959"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15959"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15959"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}