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图形处理器上的耦合簇理论 I. 耦合簇双激发方法。

Coupled Cluster Theory on Graphics Processing Units I. The Coupled Cluster Doubles Method.

作者信息

DePrince A Eugene, Hammond Jeff R

机构信息

Center for Nanoscale Materials and ‡Leadership Computing Facility, Argonne National Laboratory , 9700 South Cass Avenue, Argonne, Illinois 60439, United States.

出版信息

J Chem Theory Comput. 2011 May 10;7(5):1287-95. doi: 10.1021/ct100584w. Epub 2011 Apr 15.

Abstract

The coupled cluster (CC) ansatz is generally recognized as providing one of the best wave function-based descriptions of electronic correlation in small- and medium-sized molecules. The fact that the CC equations with double excitations (CCD) may be expressed as a handful of dense matrix-matrix multiplications makes it an ideal method to be ported to graphics processing units (GPUs). We present our implementation of the spin-free CCD equations in which the entire iterative procedure is evaluated on the GPU. The GPU-accelerated algorithm readily achieves a factor of 4-5 speedup relative to the multithreaded CPU algorithm on same-generation hardware. The GPU-accelerated algorithm is approximately 8-12 times faster than Molpro, 17-22 times faster than NWChem, and 21-29 times faster than GAMESS for each CC iteration. Single-precision GPU-accelerated computations are also performed, leading to an additional doubling of performance. Single-precision errors in the energy are typically on the order of 10(-6) hartrees and can be improved by about an order of magnitude by performing one additional iteration in double precision.

摘要

耦合簇(CC)近似通常被认为是对中小分子电子相关性基于波函数的最佳描述之一。具有双激发的CC方程(CCD)可以表示为少量密集的矩阵 - 矩阵乘法,这使得它成为移植到图形处理单元(GPU)的理想方法。我们展示了无自旋CCD方程的实现,其中整个迭代过程在GPU上进行评估。相对于同一代硬件上的多线程CPU算法,GPU加速算法很容易实现4到5倍的加速。对于每次CC迭代,GPU加速算法比Molpro快约8 - 12倍,比NWChem快17 - 22倍,比GAMESS快21 - 29倍。还进行了单精度GPU加速计算,从而使性能额外提高一倍。能量的单精度误差通常在10^(-6)哈特里量级,通过再进行一次双精度迭代可以提高大约一个数量级。

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