A Scalable High-Performance I/O System for a Numerical Weather Forecast Model on the Cubed-Sphere Grid
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The design and implementation of a high-performance Input/Output (I/O) library for the Korean Integrated Model (KIM, KIM-IO) is described in this paper. The KIM is a next-generation global operational model for the Korea Meteorological Administration (KMA). The horizontal discretization of KIM consists of the spectral-element method on the cubed-sphere grid. The KIM-IO is developed to be a consistent and efficient approach for input and output of essential data in this particular grid structure in a multiprocessing environment. The KIM-IO provides three main features, comprising the sequential I/O, parallel I/O, and I/O decomposition methods, and adopts user-friendly interfaces similar to the Network Common Data Form (NetCDF). The efficiency of the KIM-IO is verified using experiments to analyze the performance of its three features. The scalability is also verified by implementing the KIMIO in the KIM at a resolution of approximately 12 km using the 4th supercomputer of KMA. The experimental results show that both regular parallel I/O and sequential I/O undergo performance degradation with an increasing number of processes. However, the I/O decomposition method in the KIM-IO overcomes this degradation, leading to improvement in scalability. The results also indicate that with using the new I/O decomposition method, the KIM attains good parallel scalability up to Ο (100,000) cores.
Key wordsParallel I/O cubed-sphere grid I/O decomposition high-performance
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- Dennis, J. M., 2003: Partitioning with space-filling curves on the cubedsphere. Proc. the Workshop on Massively Parallel Processing at IPDPS’03, Nice, France, IPDPS.Google Scholar
- Dennis, J. M., J. Edwards, K. J. Evans, O. N. Guba, P. H. Lauritzen, A. A. Mirin, A. St-Cyr, M. A. Taylor, and P. H. Worly, 2011: CAM-SE: A scalable spectral element dynamical core for the community atmosphere model. Int. J. High Perform. Comput. Appl., 26, 74-89, doi:10.1177/109434-2011428142.CrossRefGoogle Scholar
- Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) System for global weather forecasting (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0028-9.Google Scholar
- Li, J., and Coauthors, 2003: Parallel netCDF: A high-performance scientific I/O interface. Proc. the ACM/IEEE Conference on Supercomputing (SC), Phoenix, AZ, USA, ACM, 11 pp.Google Scholar
- Message Passing Interface Forum, 2008: MPI: A Message-Passing Interface Standard Version 2.1. [Available online at https://www.mpiforum. org/docs/mpi-2.1/mpi21-report.pdf].Google Scholar
- Meswani, M. R., M. A. Laurenzano, L. Carrington, and A. Snavely, 2010: Modeling and Predicting Disk I/O Time of HPC Applications. Proc. 2010 DoD High Performance Computing Modernization Program Users Group Conference, Schaumburg, IL, USA, HPCMP-UGC, 476-486.Google Scholar
- Shalf, J., K. Asanovi, D. Patterson, K. Keutzer, T. Mattson, and K. Yelick, 2009: The Manycore Revolution: Will HPC Lead or Follow? SciDAC Review, 14, 40-49.Google Scholar
- Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A Description of the Advanced Research WRF Version 3. NCAR Tech. Note. NCAR/TN-475+STR, 113 pp.Google Scholar
- Sunderam, V. S., and S. A. Moyer, 1996: Parallel I/O for distributed systems: Issues and implementation. Parallel Comput., 12, 25-38, doi: 10.1016/0167-739X(95)00033-O.Google Scholar
- Zou, Y., W. Xue, and S. Liu, 2014: A case study of large-scale parallel I/O analysis and optimization for numerical weather prediction system. Parallel Comput., 37, 378-389, doi:10.1016/j.future.2013.12.039.Google Scholar