D. E. Shaw Research, New York, New York 10036, USA.
J Chem Phys. 2020 Feb 28;152(8):084113. doi: 10.1063/1.5129393.
The evaluation of electrostatic energy for a set of point charges in a periodic lattice is a computationally expensive part of molecular dynamics simulations (and other applications) because of the long-range nature of the Coulomb interaction. A standard approach is to decompose the Coulomb potential into a near part, typically evaluated by direct summation up to a cutoff radius, and a far part, typically evaluated in Fourier space. In practice, all decomposition approaches involve approximations-such as cutting off the near-part direct sum-but it may be possible to find new decompositions with improved trade-offs between accuracy and performance. Here, we present the u-series, a new decomposition of the Coulomb potential that is more accurate than the standard (Ewald) decomposition for a given amount of computational effort and achieves the same accuracy as the Ewald decomposition with approximately half the computational effort. These improvements, which we demonstrate numerically using a lipid membrane system, arise because the u-series is smooth on the entire real axis and exact up to the cutoff radius. Additional performance improvements over the Ewald decomposition may be possible in certain situations because the far part of the u-series is a sum of Gaussians and can thus be evaluated using algorithms that require a separable convolution kernel; we describe one such algorithm that reduces communication latency at the expense of communication bandwidth and computation, a trade-off that may be advantageous on modern massively parallel supercomputers.
在分子动力学模拟(和其他应用)中,对周期性晶格中一组点电荷的静电能进行评估是计算成本很高的部分,这是因为库仑相互作用具有长程性质。一种标准方法是将库仑势分解为近部分,通常通过直接求和到截止半径来评估,以及远部分,通常在傅里叶空间中评估。在实践中,所有分解方法都涉及到近似 - 例如截止近部分的直接求和 - 但可能有可能找到新的分解,在准确性和性能之间实现更好的权衡。在这里,我们提出了 u 级数,这是库仑势的一种新分解,与给定计算工作量的标准(Ewald)分解相比更加准确,并以大约一半的计算工作量实现与 Ewald 分解相同的准确性。这些改进是通过使用脂质膜系统进行数值演示的,因为 u 级数在整个实轴上是平滑的,并且在截止半径处是精确的。由于 u 级数的远部分是高斯函数的和,因此在某些情况下,相对于 Ewald 分解可能会有更好的性能改进,因为可以使用需要可分离卷积核的算法来评估 u 级数的远部分;我们描述了一种这样的算法,它以牺牲通信带宽和计算为代价降低了通信延迟,这种权衡在现代大规模并行超级计算机上可能是有利的。