From HPC Performance to Climate Modeling: Transforming Methods for HPC Predictions into Models of Extreme Climate Conditions

Abstract

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.