{"id":10548,"date":"2013-09-18T23:27:53","date_gmt":"2013-09-18T20:27:53","guid":{"rendered":"http:\/\/hgpu.org\/?p=10548"},"modified":"2013-09-18T23:27:53","modified_gmt":"2013-09-18T20:27:53","slug":"a-distributed-computing-approach-to-improve-the-performance-of-the-parallel-ocean-program-v2-1","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=10548","title":{"rendered":"A distributed computing approach to improve the performance of the Parallel Ocean Program (v2.1)"},"content":{"rendered":"<p>The Parallel Ocean Program (POP) is used in many strongly eddying ocean circulation simulations. Ideally one would like to do thousand-year long simulations, but the current performance of POP prohibits this type of simulations. In this work, using a new distributed computing approach, two innovations to improve the performance of POP are presented. The first is a new block partitioning scheme for the optimization of the load balancing of POP such that it can be run efficiently in a multi-platform setting. The second is an implementation of part of the POP model code on Graphics Processing Units. We show that the combination of both innovations leads to a substantial performance increase also when running POP simultaneously over multiple computational platforms.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Parallel Ocean Program (POP) is used in many strongly eddying ocean circulation simulations. Ideally one would like to do thousand-year long simulations, but the current performance of POP prohibits this type of simulations. In this work, using a new distributed computing approach, two innovations to improve the performance of POP are presented. The first [&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":[89,303,192,3],"tags":[14,510,1801,1798,20,1390],"class_list":["post-10548","post","type-post","status-publish","format-standard","hentry","category-nvidia-cuda","category-earth-and-space-sciences","category-geoscience","category-paper","tag-cuda","tag-distributed-computing","tag-earth-and-space-sciences","tag-geoscience","tag-nvidia","tag-tesla-k20"],"views":2282,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10548","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=10548"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/10548\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10548"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=10548"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=10548"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}