一种高效的CPU-GPU混合计算平台分子动力学模拟算法

Dapu Li, Wei Ai, Yu Ye, Jie Liang
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引用次数: 1

摘要

本文提出了一种基于CPU-GPU混合平台的高效并行算法,以实现金属凝固过程的大规模分子动力学模拟。结果表明,在CPU- gpu混合平台上实现的并行算法程序比在CPU集群平台上运行的基于先前算法的程序具有更好的性能。相比之下,新程序的总执行时间明显减少了。特别是,由于使用了改进的负载均衡方法,邻居列表更新时间几乎为零。与基于MPI+OpenMP模型的并行程序相比,基于CUDA+OpenMP模型的并行程序的16核计算速度提高了6倍,并且在包含10,000,000个铝原子的仿真系统中达到了最佳计算效率。最后,通过理论结果与实验结果的比较,两者之间良好的一致性有效地验证了算法的正确性。
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A efficient algorithm for molecular dynamics simulation on hybrid CPU-GPU computing platforms
In this article, an efficient parallel algorithm for a hybrid CPU-GPU platform is proposed to enable large-scale molecular dynamics (MD) simulations of the metal solidification process. The results, implemented the parallel algorithm program on the hybrid CPU-GPU platform shows better performance than the program based on previous algorithms running on the CPU cluster platform. By contrast, the total execution time of the new program has been obviously decreased. Particularly, because of the use of the modified load balancing method, the neighbor list update time is approximately zero. The parallel program based on the CUDA+OpenMP model shows a factor of 6 16-core calculation speedups compared to the parallel program based on the MPI+OpenMP model, and the optimal computational efficiency is achieved in the simulation system including 10,000,000 aluminum atoms. Finally, the good consistency between them verifies the correctness of the algorithm efficiently, by comparison of the theoretical results and experimental results.
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