Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916322365931520 |
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| author | Li, Rui Sun, Qiming Zhang, Xing Chan, Garnet Kin-Lic |
| author_facet | Li, Rui Sun, Qiming Zhang, Xing Chan, Garnet Kin-Lic |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework Li, Rui Sun, Qiming Zhang, Xing Chan, Garnet Kin-Lic Computational Physics Materials Science Chemical Physics Quantum Physics 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. |
| title | Introducing GPU-acceleration into the Python-based Simulations of Chemistry Framework |
| topic | Computational Physics Materials Science Chemical Physics Quantum Physics |
| url | https://arxiv.org/abs/2407.09700 |