This article provides an enhanced parallelization of the WSM7 microphysics scheme for the Weather Research and Forecasting Model (WRF). The parallelization is designed to maximize the utilization of a heterogeneous computing system consisting of CPUs, GPUs or both. Therefore the reference implementation of the WSM7 scheme is re-implemented for the heterogeneous execution model. For each time step, a dynamic load distribution is introduced which balances the computational load between the two components aiming for an overall minimum execution time. The evaluation of the parallelized implementation is done for a specific weather situation. Specifically, the precipitation of the low-pressure zone “Bernd” from July 2021 is simulated using an Intel Core i7-7700 CPU and a NVIDIA GTX 1070 GPU. The results show a speedup of up to 28.51 for the GPU version in comparison with the reference implementation. The heterogeneous dynamic load balancing increases the speedup achieved even further by introducing a distribution factor that is updated for each time step.

本文为天气研究和预报模型 (WRF) 提供了 WSM7 微物理方案的增强并行化。并行化旨在最大限度地利用由 CPU、GPU 或两者组成的异构计算系统。因此针对异构执行模型重新实现了WSM7方案的参考实现。对于每个时间步长，引入动态负载分配，平衡两个组件之间的计算负载，以实现总体最小执行时间。并行实施的评估是针对特定的天气情况进行的。具体来说，使用Intel Core i7-7700 CPU和NVIDIA GTX 1070 GPU模拟了2021年7月以来低压区“Bernd”的降水。结果显示，与参考实现相比，GPU 版本的加速高达 28.51。异构动态负载平衡通过引入针对每个时间步长更新的分布因子进一步提高了所实现的加速。