{"id":10986,"date":"2013-11-29T09:32:35","date_gmt":"2013-11-29T07:32:35","guid":{"rendered":"http:\/\/hgpu.org\/?p=10986"},"modified":"2013-11-29T09:32:35","modified_gmt":"2013-11-29T07:32:35","slug":"highly-optimized-full-gpu-acceleration-of-non-hydrostatic-weather-model-scale-les","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10986","title":{"rendered":"Highly Optimized Full GPU-Acceleration of Non-hydrostatic Weather Model SCALE-LES"},"content":{"rendered":"<p>SCALE-LES is a non-hydrostatic weather model developed at RIKEN, Japan. It is intended to be a global high- resolution model that would be scaled to exascale systems. This paper introduces the full GPU acceleration of all SCALE-LES modules. Moreover, the paper demonstrates the strategies to handle the unique challenges of accelerating SCALE-LES using GPU. The proposed acceleration is important for identifying the expectations and requirements of scaling SCALE-LES, and similar real world applications, into the exascale era. The GPU implementation includes the optimized GPU acceleration of SCALE-LES for a single GPU with both CUDA Fortran and OpenACC. It also includes scaling SCALE-LES for GPU- accelerated clusters. The results and analysis show how the optimization strategies affect the performance gain in SCALE- LES when moving from conventional CPU clusters towards GPU- powered clusters.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>SCALE-LES is a non-hydrostatic weather model developed at RIKEN, Japan. It is intended to be a global high- resolution model that would be scaled to exascale systems. This paper introduces the full GPU acceleration of all SCALE-LES modules. Moreover, the paper demonstrates the strategies to handle the unique challenges of accelerating SCALE-LES using GPU. The [&hellip;]<\/p>\n","protected":false},"author":332,"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":[89,303,3],"tags":[14,1801,20,1321,1390,1341,617],"class_list":["post-10986","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-earth-and-space-sciences","category-paper","tag-cuda","tag-earth-and-space-sciences","tag-nvidia","tag-openacc","tag-tesla-k20","tag-tesla-m2075","tag-weather-prediction-model"],"views":3145,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10986","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\/332"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10986"}],"version-history":[{"count":2,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10986\/revisions"}],"predecessor-version":[{"id":10988,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10986\/revisions\/10988"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10986"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10986"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10986"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}