{"id":15229,"date":"2016-01-07T00:03:01","date_gmt":"2016-01-06T22:03:01","guid":{"rendered":"http:\/\/hgpu.org\/?p=15229"},"modified":"2016-01-07T00:03:01","modified_gmt":"2016-01-06T22:03:01","slug":"computationally-efficient-tsunami-modelling-on-graphics-processing-units-gpu","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=15229","title":{"rendered":"Computationally Efficient Tsunami Modelling on Graphics Processing Units (GPU)"},"content":{"rendered":"<p>Tsunamis generated by earthquakes commonly propagate as long waves in the deep ocean and develop into sharp-fronted surges moving rapidly towards the coast in shallow water, which may be effectively simulated by hydrodynamic models solving the nonlinear shallow water equations (SWEs). However, most of the existing tsunami models suffer from long simulation time for large-scale real-world applications. In this work, a graphics processing unit (GPU) accelerated finite volume shock-capturing hydrodynamic model is presented for computationally efficient tsunami simulations. The improved performance of the GPU-accelerated tsunami model is demonstrated through a laboratory benchmark test and a field-scale simulation.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tsunamis generated by earthquakes commonly propagate as long waves in the deep ocean and develop into sharp-fronted surges moving rapidly towards the coast in shallow water, which may be effectively simulated by hydrodynamic models solving the nonlinear shallow water equations (SWEs). However, most of the existing tsunami models suffer from long simulation time for large-scale [&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":[89,303,104,3],"tags":[14,1801,1795,20,1341],"class_list":["post-15229","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-earth-and-space-sciences","category-fluid-dynamics","category-paper","tag-cuda","tag-earth-and-space-sciences","tag-fluid-dynamics","tag-nvidia","tag-tesla-m2075"],"views":3045,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15229","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=15229"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/15229\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15229"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15229"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15229"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}