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在图形处理单元上高效实现有效核势积分和梯度

Efficient implementation of effective core potential integrals and gradients on graphical processing units.

作者信息

Song Chenchen, Wang Lee-Ping, Sachse Torsten, Preiss Julia, Presselt Martin, Martínez Todd J

机构信息

Department of Chemistry and the PULSE Institute, Stanford University, Stanford, California 94305, USA.

Institute for Physical Chemistry, Friedrich Schiller University Jena, Helmholtzweg 4, 07743 Jena, Germany.

出版信息

J Chem Phys. 2015 Jul 7;143(1):014114. doi: 10.1063/1.4922844.

Abstract

Effective core potential integral and gradient evaluations are accelerated via implementation on graphical processing units (GPUs). Two simple formulas are proposed to estimate the upper bounds of the integrals, and these are used for screening. A sorting strategy is designed to balance the workload between GPU threads properly. Significant improvements in performance and reduced scaling with system size are observed when combining the screening and sorting methods, and the calculations are highly efficient for systems containing up to 10 000 basis functions. The GPU implementation preserves the precision of the calculation; the ground state Hartree-Fock energy achieves good accuracy for CdSe and ZnTe nanocrystals, and energy is well conserved in ab initio molecular dynamics simulations.

摘要

通过在图形处理单元(GPU)上实现,有效核势积分和梯度评估得以加速。提出了两个简单公式来估计积分的上限,并将其用于筛选。设计了一种排序策略以适当平衡GPU线程之间的工作量。当结合筛选和排序方法时,观察到性能有显著提升且随着系统大小的缩放比例降低,对于包含多达10000个基函数的系统,计算效率很高。GPU实现保留了计算的精度;对于CdSe和ZnTe纳米晶体,基态哈特里 - 福克能量具有良好的准确性,并且在从头算分子动力学模拟中能量得到了很好的守恒。

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