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arXiv 1708.00744physics.comp-phcond-mat.mtrl-sci
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高效率量子蒙特卡罗的延迟斯莱特行列式更新算法

Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

T. McDaniel, E. F. D'Azevedo, Y. W. Li, K. Wong, P. R. C. Kent

摘要:

在从头算量子蒙特卡罗模拟中,大型系统的主要数值代价是计算试验波函数中斯莱特行列式的值。每个蒙特卡罗步骤都需要找到一个稠密矩阵的行列式。这是最常见的迭代评估使用秩1谢尔曼-莫里森更新计划,以避免重复显式计算的逆。因此,总的计算成本在形式上是电子数量或矩阵大小的立方。为了提高这一过程的数值效率,我们提出了一种新的多秩延迟更新方案。该策略使得概率评估能够将接受的移动应用于延迟直到预定移动次数K之后的矩阵。然后,通过矩阵-矩阵运算而不是矩阵-向量运算,将接受的事件整体应用于具有增强的算术强度和计算效率的矩阵。此过程不会改变基本的Monte Carlo抽样或其统计效率。对于接受率较高的大型系统和算法(如扩散蒙特卡罗)的计算,可以在多核CPU和GPU上获得更新时间的数量级改进。

英文摘要:

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense matrix. This is most commonly iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. The overall computational cost is therefore formally cubic in the number of electrons or matrix size. To improve the numerical efficiency of this procedure, we propose a novel multiple rank delayed update scheme. This strategy enables probability evaluation with application of accepted moves to the matrices delayed until after a predetermined number of moves, K. The accepted events are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency via matrix-matrix operations instead of matrix-vector operations. This procedure does not change the underlying Monte Carlo sampling or its statistical efficiency. For calculations on large systems and algorithms such as diffusion Monte Carlo where the acceptance ratio is high, order of magnitude improvements in the update time can be obtained on both multi-core CPUs and GPUs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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