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Maximizing the Performance of the Weather Research and Forecast Model over the Hawaiian Islands

机译:最大限度地提高夏威夷群岛天气预报和天气预报模型的性能

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The Hawaiian Islands consist of dramatic terrain changes over short distances, resulting in a variety of microclimates in close proximity. To handle these challenging conditions, weather models must be run at very fine vertical and horizontal resolutions to produce accurate forecasts. Computational demands require Weather Research and Forecasting (WRF) to be executed in parallel on the Maui High Performance Computing Center''s Mana system, a Power Edge M610 Linux cluster. This machine has 1,152 compute nodes, each with two 2.8GHz quad-core Intel® Nehalem processors and 24GB RAM. Realizing maximum performance on Mana relied on the determination of an optimal number of cores to use per socket, the efficiency of an Message Passing Interface (MPI)-only implementation, an optimal set of parameters for adaptive time-stepping, a way to meet the strict stability requirements necessary for Hawaii, effective choices for processor and memory affinity, and parallel automation techniques for producing forecast imagery.
机译:夏威夷群岛由短距离的剧烈地形变化组成,导致附近的各种微气候。为了应对这些具有挑战性的条件,必须在非常精细的垂直和水平分辨率下运行天气模型,才能产生准确的预报。计算需求要求在Maui高性能计算中心的Mana系统(Power Edge M610 Linux群集)上并行执行天气研究和预报(WRF)。该计算机具有1,152个计算节点,每个计算节点配备两个2.8GHz四核Intel®Nehalem处理器和24GB RAM。在Mana上实现最大性能取决于确定每个套接字要使用的最佳内核数,仅消息传递接口(MPI)的实现效率,用于自适应时间步长的最佳参数集,一种满足以下要求的方法:夏威夷所必需的严格稳定性要求,处理器和内存关联性的有效选择以及用于生成预测图像的并行自动化技术。

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