AGCM-3DLF: Accelerating Atmospheric General Circulation Model via 3D Parallelization and Leap-Format

TL;DR

This paper introduces AGCM-3DLF, a highly scalable 3D atmospheric model that leverages leap-format and parallelization techniques to significantly improve high-resolution climate simulations on supercomputers.

Contribution

The paper presents a novel 3D decomposition and leap-format scheme for atmospheric modeling, enabling unprecedented scalability and efficiency in high-resolution climate simulations.

Findings

Scales to 196,608 CPU cores with 11.1 SYPD at 25KM resolution.

Achieves 1.06 million cores scalability with 36.1% efficiency.

Demonstrates effective large-scale simulation performance on supercomputers.

Abstract

The Atmospheric General Circulation Model (AGCM) has been an important research tool in the study of climate change for decades. As the demand for high-resolution simulation is becoming urgent, the scalability and simulation efficiency is faced with great challenges, especially for the latitude-longitude mesh-based models. In this paper, we propose a highly scalable 3D atmospheric general circulation model based on leap-format, namely AGCM-3DLF. Firstly, it utilizes a 3D decomposition method allowing for parallelism release in all three physical dimensions. Then the leap-format difference computation scheme is adopted to maintain computational stability in grid updating and avoid additional filtering at the high latitudes. A novel shifting window communication algorithm is designed for parallelization of the unified model. Furthermore, a series of optimizations are conducted to improve the effectiveness of large-scale simulations. Experiment results in different platforms demonstrate good efficiency and scalability of the model. AGCM-3DLF scales up to the entire CAS-Xiandao1 supercomputer (196,608 CPU cores), attaining the speed of 11.1 simulation-year-per-day (SYPD) at a high resolution of 25KM. In addition, simulations conducted on the Sunway TaihuLight supercomputer exhibit a 1.06 million cores scalability with 36.1% parallel efficiency.