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Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

Runge-Kutta Method and WSM6 Microphysics for Weather Prediction on Hybrid Parallel Platform

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DOI: http://dx.doi.org/10.15439/2023F7199

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 1137–1141 ()

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Abstract. In Numerical Weather Prediction models we need to model the dynamics of the atmosphere (pressure, temperature, wind, water vapor) and the physical variables (clouds, radiation, precipitation, and others) that take place in a moment. To solve the complex set of model equations on computers different numerical methods can be employed. In grid point models the temporal evolution of the model variables are calculated in a three-dimensional spatial grid which covers the atmosphere from the surface up to a given model top. Most important numerical weather prediction models include two import routine namely Runge-Kutta numerical method and microphysics scheme WSM6. They have been used to obtain relevant information about rain, snow, temperature and others. The Runge-Kutta numerical method is an important routine and it is a very time-consuming task of the model related to the dynamical core, that is responsible for integration in time. Another important routine is the WSM6 microphysics. It is responsible to calculates several hydrometeors variables and is the most consuming time routine in the entire process. Due to them importance, several parallel methods have been proposed to the problem in order to gain time performance. This paper describes the improvement of the computational performance of the Runge-Kutta method and WSM6 micro- physics by exploiting multi-level parallelism using CUDA-based Graphics Processing Unit (GPU) on hybrid parallel platform. We applied pipeline parallelism technique, workload balancing and asynchronous data exchange strategy each demonstrating be useful to improve performance. Our experimental tests show that we achieved speedup ranging from 10 to 39.3 for the Runge-Kutta method and from 5.68 to 26.39 for the WSM6 microphysics when compared to a 12- thread CPU. The pipeline parallelism and asynchronous data exchange strategies improved performance by up to 37\%. We also performed a study about the accuracy of the proposed implementation with good results.

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