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首页> 外文会议>IEEE International Conference on eScience >From HPC Performance to Climate Modeling: Transforming Methods for HPC Predictions into Models of Extreme Climate Conditions
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From HPC Performance to Climate Modeling: Transforming Methods for HPC Predictions into Models of Extreme Climate Conditions

机译：从HPC性能到气候建模：将HPC预测的方法转化为极端气候条件的模型

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In the past forty years, the high-performance computing (HPC) community has been developing powerful and rigorous tools for predicting the performance of supercomputers from log traces. In this paper, we transform one of these approaches previously used for predicting idle resources in high-end clusters into a method for capturing extreme climate events in geographical locations of interest. Our method uses an analysis based on empirical cumulative distribution functions (ECDFs) to benchmark and model occurrences of climate events including extreme temperature and precipitation. The method comprises two phases: a learning phase and a prediction phase. The learning phase applies the ECDF-based empirical analysis to historical climate data in order to identify suitable modeling and forecasting windows, both given in years. The prediction phase applies the modeling window to the most recent climate data in order to estimate the likelihood that given portions of the region of interest can experience extreme climate events in the forecasting window. The research is the first of its kind to extend HPC performance modeling techniques to study extreme climate events.
机译：在过去的四十年中，高性能计算（HPC）社区一直在开发强大而严谨的工具，用于预测从日志迹线的超级计算机的性能。在本文中，我们以前用于预测高端集群中的空闲资源的这些方法之一，以捕获感兴趣的地理位置中的极端气候事件的方法。我们的方法使用基于经验累积分配功能（ECDF）的分析，以便基准和模型发生的气候事件，包括极端温度和降水。该方法包括两个阶段：学习阶段和预测阶段。学习阶段将基于ECDF的实证分析应用于历史气候数据，以确定多年来给出的合适的建模和预测窗口。预测阶段将建模窗口应用于最近的气候数据，以估计所感兴趣区域的聚合的可能性可以体验预测窗口中的极端气候事件。该研究是延长HPC性能建模技术的首先，以研究极端气候事件。

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