High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

Abstract

Large-scale deep learning benefits from an emerging class of AI accelerators. Some of these accelerators' designs are general enough for compute-intensive applications beyond AI and Cloud TPU is one such example. In this paper, we demonstrate a novel approach using TensorFlow on Cloud TPU to simulate the two-dimensional Ising Model. TensorFlow and Cloud TPU framework enable the simple and readable code to express the complicated distributed algorithm without compromising the performance. Our code implementation fits into a small Jupyter Notebook and fully utilizes Cloud TPU's efficient matrix operation and dedicated high speed inter-chip connection. The performance is highly competitive: it outperforms the best published benchmarks to our knowledge by 60% in single-core and 250% in multi-core with good linear scaling. When compared to Tesla V100 GPU, the single-core performance maintains a ~10% gain. We also demonstrate that using low precision arithmetic---bfloat16---does not compromise the correctness of the simulation results.

26 pages, 8 figures, 7 tables. Results (excluding new results in 7.2) published in ACM SC2019 Proceedings: ACM ISBN 978-1-4503-6229-0/19/11. https://doi.org/10.1145/3295500.3356149