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

Title:
Introducing GPU Acceleration into the Python-Based Simulations of Chemistry Framework.
Authors:
Li R; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States., Sun Q; Quantum Engine LLC, Lacey, Washington 98516, United States., Zhang X; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States., Chan GK; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.
Source:
The journal of physical chemistry. A [J Phys Chem A] 2025 Feb 06; Vol. 129 (5), pp. 1459-1468. Date of Electronic Publication: 2025 Jan 23.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: American Chemical Society Country of Publication: United States NLM ID: 9890903 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-5215 (Electronic) Linking ISSN: 10895639 NLM ISO Abbreviation: J Phys Chem A Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: Washington, D.C. : American Chemical Society, c1997-
References:
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Entry Date(s):
Date Created: 20250123 Latest Revision: 20250212
Update Code:
20250212
PubMed Central ID:
PMC11808769
DOI:
10.1021/acs.jpca.4c05876
PMID:
39846468
Database:
MEDLINE

Weitere Informationen

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.