GPU acceleration of MPAS microphysics WSM6 using OpenACC directives: Performance and verification
Jae Youp Kim, Ji-Sun Kang, Minsu Joh
Abstract
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 ×5.71 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.