Journal
COMPUTERS & GEOSCIENCES
Volume 146, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2020.104627
Keywords
GPU acceleration; OpenACC; MPAS; WSM6; Numerical weather/climate model
Funding
- Korea Institute of Science and Technology Information (KISTI) [K-20-L02-C01-S01]
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This study accelerated a microphysics scheme within MPAS using OpenACC directives and focused on parallelizing WRF's microphysics scheme onto a GPU. By optimizing the performance and minimizing data transfer, significant speed-ups were achieved compared to MPI processes. A precise verification method successfully distinguished GPU-induced differences from nonlinear error growth.
In this study, we accelerated a microphysics scheme embedded within the Model for Prediction Across Scales (MPAS), using OpenACC directives. As one of the most time-consuming physics parameterization schemes, we focused on parallelizing the Weather Research and Forecasting (WRF) single-moment 6-class microphysics scheme (WSM6) onto a graphics processing unit (GPU). We applied several essential methodologies to optimize the performance of WSM6 computation on the GPU, to minimize data transfer between the central processing unit (CPU) and GPU and to reduce the waste of GPU threads during computation. As a result, we achieved GPU runs using 1 T V100 that were 2.38 times faster than 48 message passing interface processes runs, on average. When porting the whole model onto the GPU, we achieved a x5.71 speed-up in WSM6 computation, except in I/ O communication. In addition, the precise verification method distinguished nonlinear chaotic error growth from differences introduced by GPU computation, considering the characteristics of the major output variables from WSM6. We then compared the difference between the CPU and the GPU runs to the difference between CPU runs with different compilers. Moreover, we examined bias in these differences, which can distort the climatology of model simulation. Our approach successfully passed the verification process, and this represents the successful application of GPU acceleration to realistic full-model integration of MPAS.
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