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

• URL: http://arxiv.org/abs/2407.09700v1
• Date: Fri, 12 Jul 2024 21:50:19 GMT
• Title: Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework
• Authors: Rui Li, Qiming Sun, Xing Zhang, Garnet Kin-Lic Chan,
• Abstract summary: We introduce the first version of GPU4PySCF, a module that provides GPU acceleration of methods in PySCF. Benchmark calculations show a significant speedup of two orders of magnitude with respect to the multi-threaded CPU Hartree-Fock code of PySCF.
• Score: 4.368931200886271
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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