Multi-GPU implementations of the Lattice Boltzmann method are of practical interest as they allow the study of turbulent flows on large-scale simulations at high Reynolds numbers. Although programming GPUs, and in general power-efficient accelerators, typically guarantees high performances, the lack of portability in their low-level programming models implies significant efforts for maintainability and porting of applications. Directive-based models such as OpenACC look promising in tackling these aspects. In this work we will evaluate the performances of a Multi-GPU implementation of the Lattice Boltzmann method accelerated with OpenACC. The implementation will allow for multi-node simulations of fluid flows in complex geometries, also supporting heterogeneous clusters for which the load balancing problem is investigated.<\/p>\n","protected":false},"excerpt":{"rendered":"
Multi-GPU implementations of the Lattice Boltzmann method are of practical interest as they allow the study of turbulent flows on large-scale simulations at high Reynolds numbers. Although programming GPUs, and in general power-efficient accelerators, typically guarantees high performances, the lack of portability in their low-level programming models implies significant efforts for maintainability and porting of […]<\/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":[104,3],"tags":[1795,452,108,242,20,1321,1740,390],"class_list":["post-14655","post","type-post","status-publish","format-standard","hentry","category-fluid-dynamics","category-paper","tag-fluid-dynamics","tag-heterogeneous-systems","tag-lattice-boltzmann-model","tag-mpi","tag-nvidia","tag-openacc","tag-tesla-k80","tag-thesis"],"views":2634,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14655","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=14655"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/14655\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14655"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14655"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14655"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}