并行gpu加速自适应网格细化二元合金凝固过程枝晶生长三维相场模拟

Shinji Sakane, Tomohiro Takaki, Takayuki Aoki
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引用次数: 16

摘要

在合金凝固过程中枝晶生长的相场模拟中,当扩散长度明显大于枝晶尖端曲率半径时,计算成本会变得非常高。在这种情况下,自适应网格细化(AMR)方法是提高计算性能的有效方法。在本研究中,我们进行了三维枝晶生长相场模拟,其中AMR通过使用多个图形处理单元(gpu)的并行计算实现,提供了高并行计算性能。在并行GPU计算中,我们将动态负载均衡应用于并行计算,以平衡每个GPU的计算成本。通过对二元合金定向凝固过程中柱状枝晶生长的单gpu计算,验证了AMR细化条件的准确性。接下来,我们通过使用移动帧算法对三种不同的定向凝固模拟进行多gpu并行计算来评估动态负载平衡的效率。最后,进行了弱缩放测试,以验证所开发代码的并行效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parallel-GPU-accelerated adaptive mesh refinement for three-dimensional phase-field simulation of dendritic growth during solidification of binary alloy

In the phase-field simulation of dendrite growth during the solidification of an alloy, the computational cost becomes extremely high when the diffusion length is significantly larger than the curvature radius of a dendrite tip. In such cases, the adaptive mesh refinement (AMR) method is effective for improving the computational performance. In this study, we perform a three-dimensional dendrite growth phase-field simulation in which AMR is implemented via parallel computing using multiple graphics processing units (GPUs), which provide high parallel computation performance. In the parallel GPU computation, we apply dynamic load balancing to parallel computing to equalize the computational cost per GPU. The accuracy of an AMR refinement condition is confirmed through the single-GPU computations of columnar dendrite growth during the directional solidification of a binary alloy. Next, we evaluate the efficiency of dynamic load balancing by performing multiple-GPU parallel computations for three different directional solidification simulations using a moving frame algorithm. Finally, weak scaling tests are performed to confirm the parallel efficiency of the developed code.

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期刊介绍: Journal of Materials Science: Materials Theory publishes all areas of theoretical materials science and related computational methods. The scope covers mechanical, physical and chemical problems in metals and alloys, ceramics, polymers, functional and biological materials at all scales and addresses the structure, synthesis and properties of materials. Proposing novel theoretical concepts, models, and/or mathematical and computational formalisms to advance state-of-the-art technology is critical for submission to the Journal of Materials Science: Materials Theory. The journal highly encourages contributions focusing on data-driven research, materials informatics, and the integration of theory and data analysis as new ways to predict, design, and conceptualize materials behavior.
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