This paper introduces a new large‐eddy simulation model, FastEddy®, purpose built for leveraging the accelerated and more power‐efficient computing capacity of graphics processing units (GPUs) toward adopting microscale turbulence‐resolving atmospheric boundary layer simulations into future numerical weather prediction activities. Here a basis for future endeavors with the FastEddy® model is provided by describing the model dry dynamics formulation and investigating several validation scenarios that establish a baseline of model predictive skill for canonical neutral, convective, and stable boundary layer regimes, along with boundary layer flow over heterogeneous terrain. The current FastEddy® GPU performance and efficiency gains versus similarly formulated, state‐of‐the‐art CPU‐based models is determined through scaling tests as 1 GPU to 256 CPU cores. At this ratio of GPUs to CPU cores, FastEddy® achieves 6 times faster prediction rate than commensurate CPU models under equivalent power consumption. Alternatively, FastEddy® uses 8 times less power at this ratio under equivalent CPU/GPU prediction rate. The accelerated performance and efficiency gains of the FastEddy® model permit more broad application of large‐eddy simulation to emerging atmospheric boundary layer research topics through substantial reduction of computational resource requirements and increase in model prediction rate.

中文翻译：

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FastEddy®Resident-GPU加速的大涡流仿真框架：模型制定，动态核验证和性能基准

本文介绍了一种新的大型涡流仿真模型FastEddy®，其目的是利用图形处理单元（GPU）的加速且更节能的计算能力，将微尺度湍流解析大气边界层仿真应用于未来的数值天气预报活动中。在此，通过描述模型干燥动力学公式并研究几种验证方案来为FastEddy®模型的未来工作提供基础，这些方案为规范中性，对流和稳定边界层制度以及边界层流动建立了模型预测技能的基线在异质地形上。与类似的公式相比，当前的FastEddy®GPU性能和效率提高，最新的基于CPU的模型通过扩展测试确定为1个GPU至256个CPU内核。在GPU与CPU内核的这种比例下，在等效功耗下，FastEddy®的预测速率是同类CPU模型的6倍。另外，在等效的CPU / GPU预测速率下，FastEddy®以此比例使用的功耗也要少8倍。FastEddy®模型的加速性能和效率提升通过显着减少计算资源需求和增加模型预测率，允许将大涡模拟更广泛地应用于新兴的大气边界层研究主题。在等效的CPU / GPU预测速率下，FastEddy®在此比率下所消耗的功率减少了8倍。FastEddy®模型的加速性能和效率提升通过显着减少计算资源需求和增加模型预测率，允许将大涡模拟更广泛地应用于新兴的大气边界层研究主题。在等效的CPU / GPU预测速率下，FastEddy®在此比率下所消耗的功率减少了8倍。FastEddy®模型的加速性能和效率提升通过显着减少计算资源需求和增加模型预测率，允许将大涡模拟更广泛地应用于新兴的大气边界层研究主题。