An Investigation of Computation Time Based on Domain Size in WRF Model

Aisya Nafiisyanti, Ibnu Fathrio

Abstract


Estimating WRF model computation time is necessary because of the need for domain expansion for weather prediction. However, the optimum computation time is not simply gained by enlarging the domain and adding processor numbers. Thus, an investigation was carried out to determine the correlation between computation time on domain size, number of grids, and number of processors used to run the WRF model. This study uses a collection of computation time as the data input from running the WRF model with two domain group ratios, 2: 1 and 1: 1, and various processors. Negative Exponential Function (NEF) and Power Function (PF) as exponential decay functions are evaluated to represent the curve formed from the computation time against the number of processors in one domain case. This study also evaluates the speed up and efficiency of the use of processor numbers against the tested domains. NEF represents the decrease in computation time curve in a domain case better than PF since this function has a steeper slope, better initial value, and k constant that keeps the computation time falling below 0. The computation time can be optimally saved by adding approximately eight processors at the same domain ratio with four times larger grid size and the same amount of grid number. Investigations for other domain ratios need to be carried out to determine the characteristics of computation time on the number of processors, grid size, and domain size.

Keywords


Computation time; NEF; ratio; processors; speed up.

Full Text:

PDF

References


P. Pacheco, “Why Parallel Programming?,†in An Introduction to Parallel Programming, P. S. Pacheco, Ed. San Francisco: Elsevier, 2011, pp. 1–14.

W. C. Skamarock et al., “A Description of the Advanced Research WRF Model Version 4,†Boulder, CO, USA, 2019. doi: http://dx.doi.org/10.5065/1dfh-6p97.

B. C. Ancell, A. Bogusz, M. J. Lauridsen, and C. J. Nauert, “Seeding chaos: The dire consequences of numerical noise in NWP perturbation experiments,†Bull. Am. Meteorol. Soc., vol. 99, no. 3, pp. 615–628, 2018, doi: 10.1175/BAMS-D-17-0129.1.

NCAR|UCAR, “WRF scaling and timing _ Computational and Information Systems Laboratory,†cisl.ucar.edu, 2020. https://www2.cisl.ucar.edu/resources/wrf-scaling-and-timing#scaling (accessed Oct. 26, 2020).u8u8

J. G. Powers et al., “The weather research and forecasting model: Overview, system efforts, and future directions,†Bulletin of the American Meteorological Society, vol. 98, no. 8, pp. 1717–1737, 2017.

Y. Li et al., “High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach,†Hydrol. Earth Syst. Sci., vol. 23, no. 11, pp. 4635–4659, 2019, doi: 10.5194/hess-23-4635-2019.

A. Z. Abualkishik, “A comparative study on the software architecture of WRF and other numerical weather prediction models,†J. Theor. Appl. Inf. Technol., vol. 96, no. 24, pp. 8244–8254, 2018, [Online]. Available: https://www.researchgate.net/profile/Abedallah-Abualkishik-2/publication/330171087_a_comparative_study_on_the_software_architecture_of_wrf_and_other_numerical_weather_prediction_models/links/5c311530299bf12be3b1c396/a-comparative-study-on-the-SOFTWARE-ARC.

Y. Kim, K. Sartelet, J. C. Raut, and P. Chazette, “Evaluation of the Weather Research and Forecast/Urban Model Over Greater Paris,†Boundary-Layer Meteorol., vol. 149, no. 1, pp. 105–132, 2013, doi: 10.1007/s10546-013-9838-6.

H. Duan, Y. Li, T. Zhang, Z. Pu, C. Zhao, and Y. Liu, “Evaluation of the Forecast Accuracy of Near-Surface Temperature and Wind in Northwest China Based on the WRF Model,†J. Meteorol. Res., vol. 32, no. 3, pp. 469–490, 2018, doi: 10.1007/s13351-018-7115-9.

S. Sharma, R. Siddique, S. Reed, P. Ahnert, and A. Mejia, “Hydrological model diversity enhances streamflow forecast skill at short-to medium-range timescales,†Water Resour. Res., vol. 55, no. 2, pp. 1510–1530, 2019, doi: 10.1029/2018WR023197.

S. Höfinger, T. Ruh, and E. Haunschmid, “Fast Approximate Evaluation of Parallel Overhead from a Minimal Set of Measured Execution Times,†Parallel Process. Lett., vol. 28, no. 1, pp. 1–12, 2018, doi: 10.1142/S0129626418500032.

W. Wu, L. He, W. Lin, R. Mao, and S. Jarvis, “SAFA: A semi-asynchronous protocol for fast federated learning with low overhead,†IEEE Trans. Comput., vol. 70, no. 5, pp. 1–16, 2019, doi: 10.1109/tc.2020.2994391.

D. Meyer et al., “WRF-TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model,†J. Adv. Model. Earth Syst., vol. 12, no. 8, p. 18, 2020, doi: 10.1029/2019MS001961.

A. Golzio, S. Ferrarese, C. Cassardo, G. Adele, and D. Manuela, “Land-Use Improvements in the Weather Research and Forecasting Model over Complex Mountainous Terrain and Comparison of Different Grid Sizes,†Boundary-Layer Meteorol., p. 33, 2021, doi: 10.1007/s10546-021-00617-1.

H. Schmitz, “Schnek: A C++ library for the development of parallel simulation codes on regular grids,†Comput. Phys. Commun., vol. 226, pp. 151–164, 2018, doi: 10.1016/j.cpc.2017.12.023.

D. Koo et al., “An empirical study of I/O separation for burst buffers in HPC systems,†J. Parallel Distrib. Comput., vol. 148, pp. 96–108, 2021, doi: 10.1016/j.jpdc.2020.10.007.

C. Hollowell, J. Barnett, C. Caramarcu, W. Strecker-Kellogg, A. Wong, and A. Zaytsev, “Mixing HTC and HPC Workloads with HTCondor and Slurm,†J. Phys. Conf. Ser., vol. 898, no. 8, p. 8, 2017, doi: 10.1088/1742-6596/898/8/082014.

N. Malitsky et al., “Building near-real-time processing pipelines with the spark-MPI platform,†in 2017 New York Scientific Data Summit (NYSDS), 2017, pp. 1–8, doi: 10.1109/NYSDS.2017.8085039.

E. R. Cook, S. G. Shiyatov, V. S. Mazepa, A. Ecology, and U. Branch, Treering standardization and growth-trend estimation . In .: Cook E . Kairiukstis L . ( eds .), no. April 2016. Springer-Science+Business Media, B. V., 1990.

P. Singh, B. Khan, A. Vidyarthi, H. H. Alhelou, and P. Siano, “Energy-aware online non-clairvoyant scheduling using speed scaling with arbitrary power function,†Appl. Sci., vol. 9, no. 7, 2019, doi: 10.3390/app9071467.

Y. Monno, D. Kiku, M. Tanaka, and M. Okutomi, “Adaptive residual interpolation for color and multispectral image demosaicking,†Sensors (Switzerland), vol. 17, no. 12, pp. 1–21, 2017, doi: 10.3390/s17122787.

E. V. Konopatskiy and A. A. Bezditnyi, “Geometric modeling of multifactor processes and phenomena by the multidimensional parabolic interpolation method,†J. Phys. Conf. Ser., vol. 1441, no. 1, 2020, doi: 10.1088/1742-6596/1441/1/012063.

Z. Ye, W. Zhou, L. Zhang, Y. Ge, K. Xiao, and Y. Deng, “Multi-user mobile sequential recommendation: An efficient parallel computing paradigm,†in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2018, pp. 2624–2633, doi: 10.1145/3219819.3220111.

R. Moreno et al., “Analysis of a New MPI Process Distribution for the Weather Research and Forecasting (WRF) Model,†Sci. Program., vol. 2020, no. i, p. 13, 2020, doi: 10.1155/2020/8148373.




DOI: http://dx.doi.org/10.18517/ijaseit.12.4.14628

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development