在基于python的化学模拟框架中引入GPU加速。

IF 2.8 2区 化学 Q3 CHEMISTRY, PHYSICAL The Journal of Physical Chemistry A Pub Date : 2025-02-06 Epub Date: 2025-01-23 DOI:10.1021/acs.jpca.4c05876
Rui Li, Qiming Sun, Xing Zhang, Garnet Kin-Lic Chan
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引用次数: 0

摘要

我们介绍了GPU4PySCF的第一个版本,这是一个为PySCF中的方法提供GPU加速的模块。作为一个核心功能,这提供了一个GPU实现的双电子排斥积分(ERIs)的压缩基集,包括多达g个函数使用Rys正交。为了说明这如何加速量子化学工作流程,我们描述了如何在积分-直接Hartree-Fock构建和核梯度构建中有效地使用ERIs。基准计算显示,相对于PySCF的多线程CPU har树- fock代码,PySCF的速度显著提高了2个数量级,并且在单个NVIDIA A100 GPU上的性能可与其他开源GPU加速量子化学软件包(包括GAMESS和QUICK)相媲美。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Introducing GPU Acceleration into the Python-Based Simulations of Chemistry Framework.

We introduce the first version of GPU4PySCF, a module that provides GPU acceleration of methods in PySCF. As a core functionality, this provides a GPU implementation of two-electron repulsion integrals (ERIs) for contracted basis sets comprising up to g functions using the Rys quadrature. As an illustration of how this can accelerate a quantum chemistry workflow, we describe how to use the ERIs efficiently in the integral-direct Hartree-Fock build and nuclear gradient construction. Benchmark calculations show a significant speedup of 2 orders of magnitude with respect to the multithreaded CPU Hartree-Fock code of PySCF and the performance comparable to other open-source GPU-accelerated quantum chemical packages, including GAMESS and QUICK, on a single NVIDIA A100 GPU.

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来源期刊
The Journal of Physical Chemistry A
The Journal of Physical Chemistry A 化学-物理:原子、分子和化学物理
CiteScore
5.20
自引率
10.30%
发文量
922
审稿时长
1.3 months
期刊介绍: The Journal of Physical Chemistry A is devoted to reporting new and original experimental and theoretical basic research of interest to physical chemists, biophysical chemists, and chemical physicists.
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