{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scalable-communication-for-high-order-stencil","title":"Scalable communication for high-order stencil computations using CUDA-aware MPI","arxiv_id":"2103.01597","date":"2021-03-02","proceeding":null,"authors":["Johannes Pekkilä","Miikka S. Väisälä","Maarit J. Käpylä","Matthias Rheinhardt","Oskar Lappi"],"abstract":"Modern compute nodes in high-performance computing provide a tremendous level of parallelism and processing power. However, as arithmetic performance has been observed to increase at a faster rate relative to memory and network bandwidths, optimizing data movement has become critical for achieving strong scaling in many communication-heavy applications. This performance gap has been further accentuated with the introduction of graphics processing units, which can provide by multiple factors higher throughput in data-parallel tasks than central processing units. In this work, we explore the computational aspects of iterative stencil loops and implement a generic communication scheme using CUDA-aware MPI, which we use to accelerate magnetohydrodynamics simulations based on high-order finite differences and third-order Runge-Kutta integration. We put particular focus on improving intra-node locality of workloads. Our GPU implementation scales strongly from one to $64$ devices at $50\\%$--$87\\%$ of the expected efficiency based on a theoretical performance model. Compared with a multi-core CPU solver, our implementation exhibits $20$--$60\\times$ speedup and $9$--$12\\times$ improved energy efficiency in compute-bound benchmarks on $16$ nodes.","url_abs":"https://arxiv.org/abs/2103.01597v2","url_pdf":"https://arxiv.org/pdf/2103.01597v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"scalable-communication-for-high-order-stencil","repo_url":"https://bitbucket.org/jpekkila/pekkila-2021-artifacts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}