The present paper develops a fast method to simulate the solidification structure of continuous billets with Cellular Automaton (CA) model. Traditional solution of the CA model on single CPU takes a long time for the massive datasets and complicated calculations, making it unrealistic to optimize the parameters through numerical simulation. In this paper, a parallel method based on Graphics Processing Units (GPU) was proposed to accelerate the calculation, which developed new algorithms for the solute redistribution and neighbor capture to avoid data race in parallel computing. This new method was applied to simulate the solidification structure of Fe-0.64C alloy, and the simulating results were in good agreement with the experiment results with the same parameters. The absolute computational time for the fast method implemented on Tesla P100 GPU is 277 s, while the traditional method implemented on Intel(R) Xeon(R) CPU E5−2680 v4 @ 2.40 GHz with single core is 24.57 h. The speedup, ratio between the absolute computational time of GPU-CA and CPU-CA, varies from 300 to 400 with the increase of the grids.

本文提出了一种利用元胞自动机（CA）模型模拟​​连续坯料凝固组织的快速方法。CA模型在单CPU上的传统解决方案花费大量时间处理海量数据集和复杂的计算，因此通过数值模拟优化参数是不现实的。本文提出了一种基于图形处理单元（GPU）的并行方法来加快计算速度，并开发了用于溶质再分配和邻域捕获的新算法，以避免并行计算中的数据竞争。采用该新方法对Fe-0.64C合金的凝固组织进行了模拟，模拟结果与参数相同的实验结果吻合较好。在Tesla P100 GPU上实现的快速方法的绝对计算时间为277 s，而在单核2.40 GHz的Intel®Xeon®CPU E5-2680 v4 @上实现的传统方法为24.57 h。随着网格的增加，GPU-CA和CPU-CA的绝对计算时间之比的加速比从300变为400。