Terascale Spectral Element Dynamical Core for Atmospheric General Circulation Models

R. Loft, Stephen J. Thomas, J. Dennis
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引用次数: 71

Abstract

Climate modeling is a grand challenge problem where scientific progress is measured not in terms of the largest problem that can be solved but by the highest achievable integration rate. These models have been notably absent in previous Gordon Bell competitions due to their inability to scale to large processor counts. A scalable and efficient spectral element atmospheric model is presented. A new semi-implicit time stepping scheme accelerates the integration rate relative to an explicit model by a factor of two, achieving 130 years per day at T63L30 equivalent resolution. Execution rates are reported for the standard shallow water and Held-Suarez climate benchmarks on IBM SP clusters. The explicit T170 equivalent multi-layer shallow water model sustains 343 Gflops at NERSC, 206 Gflops at NPACI (SDSC) and 127 Gflops at NCAR. An explicit Held-Suarez integration sustains 369 Gflops on 128 16-way IBM nodes at NERSC.
大气环流模式的太斯卡尔谱元动力核心
气候模拟是一个巨大的挑战问题,科学进步的衡量标准不是可以解决的最大问题,而是可以实现的最高整合率。这些模型在之前的Gordon Bell竞赛中明显缺席,因为它们无法扩展到大型处理器数量。提出了一种可扩展、高效的光谱元大气模型。一种新的半隐式时间步进方案将相对于显式模式的积分率提高了两倍,在T63L30等效分辨率下达到130年/天。报告了IBM SP集群上标准浅水和hold - suarez气候基准的执行速度。明确的T170等效多层浅水模型在NERSC维持343 Gflops,在NPACI (SDSC)维持206 Gflops,在NCAR维持127 Gflops。一个显式的Held-Suarez集成在NERSC的128个16路IBM节点上维持369 gflop。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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