Optimizing the HOMME dynamical core for multicore platforms. (September 2019)

• Record Type: Journal Article
• Authors: Dennis, John M; Dobbins, Brian; Kerr, Christopher; Kim, Youngsung
• Other Names: Mascagni Michael guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 33:Number 5(2019)
• Page Start: 1030
• Page End: 1045
• Publication Date: 2019-09
• Subjects: Performance engineering -- earth system modeling -- code optimization -- high-performance computing application -- dynamical core -- spectral element; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/1094342019849618
• Languages: English
• ISSNs: 1094-3420

Abstract

The approach of the next-generation computing platforms offers a tremendous opportunity to advance the state-of-the-art in global atmospheric dynamical models. We detail our incremental approach to utilize this emerging technology by enhancing concurrency within the High-Order Method Modeling Environment (HOMME) atmospheric dynamical model developed at the National Center for Atmospheric Research (NCAR). The study focused on improvements to the performance of HOMME which is a Fortran 90 code with a hybrid (MPIOpenMP) programming model. The article describes the changes made to the use of message passing interface (MPI) and OpenMP as well as single-core optimizations to achieve significant improvements in concurrency and overall code performance. For our optimization studies, we utilize the "Cori" system with an Intel Xeon Phi Knights Landing processor deployed at the National Energy Research Supercomputing Center and the "`Cheyenne" system with an Intel Xeon Broadwell processor installed at the NCAR. The results from the studies, using "workhorse" configurations performed at NCAR, show that these changes have a transformative impact on the computational performance of HOMME. Our improvements have shown that we can effectively increase potential concurrency by efficiently threading the vertical dimension. Further, we have seen a factor of two overall improvement in the computational performance of the code resulting from the single-core optimizations. Most notably from the work is that our incremental approach allows for high-impact changes without disrupting existing scientific productivity in the HOMME community.