{"id":17535,"date":"2017-09-07T10:01:34","date_gmt":"2017-09-07T07:01:34","guid":{"rendered":"https:\/\/hgpu.org\/?p=17535"},"modified":"2017-09-07T10:01:34","modified_gmt":"2017-09-07T07:01:34","slug":"from-mpi-to-mpiopenacc-conversion-of-a-legacy-fortran-pcg-solver-for-the-spherical-laplace-equation","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=17535","title":{"rendered":"From MPI to MPI+OpenACC: Conversion of a legacy FORTRAN PCG solver for the spherical Laplace equation"},"content":{"rendered":"<p>A real-world example of adding OpenACC to a legacy MPI FORTRAN Preconditioned Conjugate Gradient code is described, and timing results for multi-node multi-GPU runs are shown. The code is used to obtain three-dimensional spherical solutions to the Laplace equation. Its application is finding potential field solutions of the solar corona, a useful tool in space weather modeling. We highlight key tips, strategies, and challenges faced when adding OpenACC, including linking FORTRAN code to the cuSparse library, using CUDA-aware MPI, maintaining portability, and dealing with multi-node, multi-GPU run-time environments. Timing results are shown for the code running with MPI-only (up to 1728 CPU cores) and with MPI+OpenACC (up to 64 NVIDIA P100 GPUs). Performance portability is also addressed, including results using MPI+OpenACC for multi-core x86 CPUs.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>A real-world example of adding OpenACC to a legacy MPI FORTRAN Preconditioned Conjugate Gradient code is described, and timing results for multi-node multi-GPU runs are shown. The code is used to obtain three-dimensional spherical solutions to the Laplace equation. Its application is finding potential field solutions of the solar corona, a useful tool in space [&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":[1782,14,989,130,597,242,20,1321,1586,1931],"class_list":["post-17535","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-fortran","tag-laplace-and-poisson-equation","tag-mathematical-software","tag-mpi","tag-nvidia","tag-openacc","tag-performance-portability","tag-tesla-p100"],"views":3728,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17535","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=17535"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/17535\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17535"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17535"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17535"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}