The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)

Abstract

The weather and climate model ICON (ICOsahedral Nonhydrostatic) is being used in high-resolution climate simulations, in order to resolve small-scale physical processes. The envisaged performance for this task is 1 simulated year per day for a coupled atmosphere–ocean setup at global 1.2 km resolution. The necessary computing power for such simulations can only be found on exascale supercomputing systems. The main question we try to answer in this article is where to find sustained exascale performance, i.e. which hardware (processor type) is best suited for the weather and climate model ICON, and consequently how this performance can be exploited by the model, i.e. what changes are required in ICON's software design so as to utilize exascale platforms efficiently. To this end, we present an overview of the available hardware technologies and a quantitative analysis of the key performance indicators of the ICON model on several architectures. It becomes clear that parallelization based on the decomposition of the spatial domain has reached the scaling limits, leading us to conclude that the performance of a single node is crucial to achieve both better performance and better energy efficiency. Furthermore, based on the computational intensity of the examined kernels of the model it is shown that architectures with higher memory throughput are better suited than those with high computational peak performance. From a software engineering perspective, a redesign of ICON from a monolithic to a modular approach is required to address the complexity caused by hardware heterogeneity and new programming models to make ICON suitable for running on such machines.