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

• Published in: The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory Vol. 129; no. 5; pp. 1459 - 1468
• Main Authors: Li, Rui, Sun, Qiming, Zhang, Xing, Chan, Garnet Kin-Lic
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 06.02.2025
• Subjects: A: New Tools and Methods in Experiment and Theory; Algorithms; Basis sets; Circuits; Energy; Genetics
• ISSN: 1089-5639; 1520-5215; 1520-5215
• DOI: 10.1021/acs.jpca.4c05876

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.