arXiv Open Access 2024

Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework

Rui Li Qiming Sun Xing Zhang Garnet Kin-Lic Chan
Lihat Sumber

Abstrak

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 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 Fock build and nuclear gradient construction. Benchmark calculations show a significant speedup of two orders of magnitude with respect to the multi-threaded CPU Hartree-Fock code of PySCF, and performance comparable to other GPU-accelerated quantum chemical packages including GAMESS and QUICK on a single NVIDIA A100 GPU.

Penulis (4)

R

Rui Li

Q

Qiming Sun

X

Xing Zhang

G

Garnet Kin-Lic Chan

Format Sitasi

Li, R., Sun, Q., Zhang, X., Chan, G.K. (2024). Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework. https://arxiv.org/abs/2407.09700

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓