论文标题
迈向埃克斯卡尔进行风能模拟
Towards Exascale for Wind Energy Simulations
论文作者
论文摘要
我们检查了两个开源计算流体动力学代码的大型仿真建模方法和计算性能，以模拟与风能生产直接相关的大气边界层（ABL）流。第一个是NEKR，是高阶，非结构化网格，光谱元素代码。第二个是AMR-Wind，是具有自适应网状功能的二阶有限量代码。这项研究的目的是共同开发这些代码，以提高每个代码的模型保真度和性能。这些功能对于运行基于ABL的应用程序至关重要，例如有关高级计算体系结构的风电场分析。为此，我们使用4至800个节点（24至4,800 NVIDIA V100 GPU）和粉碎机（使用18至384的图形模型在AMD MI250X GPUS上使用18至384的图形模具，使用4到800个节点（24至4,800 NVIDIA V100 GPU），调查了NEKRS和AMR-Wind在Oak Ridge领导力设施超级计算机峰会上的性能。我们比较强大和弱尺度的功能，线性求解器性能以及解决方案的时间。我们还确定了并行缩放的领先抑制剂。
We examine large-eddy-simulation modeling approaches and computational performance of two open-source computational fluid dynamics codes for the simulation of atmospheric boundary layer (ABL) flows that are of direct relevance to wind energy production. The first is NekRS, a high-order, unstructured-grid, spectral element code. The second, AMR-Wind, is a block-structured, second-order finite-volume code with adaptive-mesh-refinement capabilities. The objective of this study is to co-develop these codes in order to improve model fidelity and performance for each. These features will be critical for running ABL-based applications such as wind farm analysis on advanced computing architectures. To this end, we investigate the performance of NekRS and AMR-Wind on the Oak Ridge Leadership Facility supercomputers Summit, using 4 to 800 nodes (24 to 4,800 NVIDIA V100 GPUs), and Crusher, the testbed for the Frontier exascale system using 18 to 384 Graphics Compute Dies on AMD MI250X GPUs. We compare strong- and weak-scaling capabilities, linear solver performance, and time to solution. We also identify leading inhibitors to parallel scaling.
