A Deep Learning-Based Consistency Test Approach for Earth System Models on Heterogeneous Many-Core Systems

Abstract

Abstract. Physical and heat limits of the semiconductor technology require the adaptation of heterogeneous architectures in supercomputers to maintain a continuous increase of computing performance. The coexistence of general-purpose cores and accelerator cores, which usually employ different hardware architectures, can lead to bit-level differences, especially when we try to maximize the performance on both kinds of cores. Such differences further lead to unavoidable computational perturbations through temporal integration, which can blend with software or human errors. Software correctness verification in the form of quality assurance is a critically important step in the development and optimization of Earth system models (ESMs) on heterogeneous many-core systems with mixed perturbations of software changes and hardware updates. We have developed a deep learning-based consistency test approach for Earth System Models referred to as ESM-DCT. The ESM-DCT is based on the unsupervised bidirectional gate recurrent unit-autoencoder (BGRU-AE) model, which can still detect the existence of software or human errors when taking hardware-related perturbations into account. We use the Community Earth System Model (CESM) on the new Sunway system as an example of large-scale ESMs to evaluate the ESM-DCT. The results show that facing with the mixed perturbations caused by hardware designs and software changes in heterogeneous computing, the ESM-DCT can detect software or human errors when determining whether or not the model simulation is consistent with the original results in homogeneous computing. Our ESM-DCT tool provides an efficient and objective approach for verifying the reliability of the development and optimization of scientific computing models on the heterogeneous many-core systems.