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The FastEddy? Resident‐GPU Accelerated Large‐Eddy Simulation Framework: Model Formulation, Dynamical‐Core Validation and Performance Benchmarks

机译:快餐?驻地GPU加速大涡仿真框架:模型配方,动态核心验证和性能基准

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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. Plain Language Summary This paper introduces a new model for atmospheric flows, FastEddy?, engineered to permit faster, more power‐efficient, and more broad engagement in simulation of atmospheric flows at high levels of spatial and temporal detail by using graphics cards for accelerating computations. A model description and set of pertinent validation efforts are provided along with performance intercomparison versus two state‐of‐the‐art and widely used models of a similar vein. The documentation of formulation and validity along with the accelerated performance and more power‐efficient capability of FastEddy? provides a comprehensive and robust basis for future adoption and extension as an enabling technology for high‐impact atmospheric boundary layer research and applications.
机译:本文介绍了用于朝向采用微尺度利用图形处理单元(GPU)的加速和更节能的计算能力建立了一个新的大涡仿真模型,FastEddy ?,目的紊流解析大气边界层模拟到未来的数字天气预测活动。这里与FastEddy未来努力的基础?模型通过描述模型干动力学制剂和调查其建立模型预测技巧的基线规范中性,对流的和稳定的边界层制度,超过异质地形边界层流沿若干验证场景设置。目前FastEddy? GPU的性能和效率的提高与类似配制的,国家的最先进的基于CPU的模型是通过缩放测试作为1个GPU 256个的CPU内核来确定。在GPU的CPU的内核这个比例,FastEddy?实现了比下相当于功耗相称的CPU型号快6倍的预测率。另外,FastEddy?使用8倍更少的功率在相同的CPU / GPU预测速率下该比率。加速的FastEddy的性能和效率?模型允许更广泛的大涡模拟的应用程序通过的计算资源需求,并在模型预测率增加显着降低新兴大气边界层的研究课题。平原语言总结本文通过使用图形卡用于加速计算引入了在高水平的空间和时间详细的大气气流的模拟大气流动,FastEddy ?,工程化,以允许更快的一个新的模型,更节能,并且更广泛参与。具有性能比对一起提供相对两种状态的最先进的相关验证工作的模型的描述和集和广泛使用了类似的静脉的模式。配方与FastEddy的加速性能和更节能的能力以及文档和有效性?为未来的采用和推广作为一个有利的技术,高影响力的大气边界层的研究和应用的全面和强大的基础。

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