MISA-AKMC : Achieve Kinetic Monte Carlo Simulation of 20 Quadrillion Atoms on GPU Clusters
Shunde Li, Zhijie Pan, Ningming Nie, Jue Wang, He Bai, Genshen Chu, Yan Zeng, Xinfu He, Yangang Wang, Changjun Hu, Xuebin Chi
摘要
The Atomic Kinetic Monte Carlo (AKMC) method provides insights into the macroscopic behavior of materials through atomistic-level simulations and finds broad applications in materials science innovation. Improving simulation scale and performance remains a consistent focus in the development of parallel AKMC software. We port the AKMC software to GPU clusters. To alleviate the memory pressure in large-scale complex system simulations, we redesign the data layout and propose the Lattice Data Compression and Vacancy Data Decompression algorithms. Additionally, We propose a multi-level pipeline scheme combined with an on-demand communication forwarding and merging strategy to reduce data transfer and communication overhead. Compared to state-of-the-art KMC software, MISA-AKMC achieves a 10.41-fold improvement in computational throughput and a 52.07-fold expansion in simulation scale. We implement the first true micrometer-scale AKMC simulation involving 20 quadrillion atoms on GPU clusters. MISA-AKMC achieves 96.03% parallel efficiency in weak scaling and 85.29% in strong scaling on 16,000 GPUs.
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