Weather Research Forecasting (WRF) model is a popular tool used in both research and operational applications that enables us to obtain relevant information about rain, snow, and others. The WRF Single-Moment 6-Class ...
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ISBN:
(纸本)9781665410168
Weather Research Forecasting (WRF) model is a popular tool used in both research and operational applications that enables us to obtain relevant information about rain, snow, and others. The WRF Single-Moment 6-Class Microphysics Scheme (WSM6) is an important routine and it is the most lime-consuming task of the WRF model. Due to its importance, several parallel methods have been proposed to the problem. The parallel approach using multiple GPU for the WSM6 scheme has allowed the address of a large number of data in a reasonable time. This paper describes the improvement of the computational performance of the WSM6 by exploiting fine-grained parallelism using the Graphics Processing Unit (GPU) with OpenACC paradigm. When compared to a 24-thread CPU, the speedup we achieve is 67.4 and 108.0 on one and four GPUs, respectively. We also compare our implementation to a recent OpenACC implementation in the literature and our proposed solution is 21 times faster than it using one GPU and 34 times faster using four GPUs. We also performed a study about the accuracy of the proposed implementation with good results.
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