Implementation of the multigrid Gaussian-Plane-Wave algorithm with GPU acceleration in PySCF
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910073765232640 |
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| author | Li, Rui Zhang, Xing Sun, Qiming Wang, Yuanheng Yang, Junjie Chan, Garnet Kin-Lic |
| author_facet | Li, Rui Zhang, Xing Sun, Qiming Wang, Yuanheng Yang, Junjie Chan, Garnet Kin-Lic |
| contents | We introduce a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting (FFTDF) approach for efficient Fock builds and nuclear gradient evaluations within Kohn-Sham density functional theory, as implemented in the GPU4PySCF module of PySCF. Our CUDA kernels employ a grid-based parallelization strategy for contracting Gaussian basis function pairs and achieve up to 80% of the FP64 peak performance on NVIDIA GPUs, with no loss of efficiency for high angular momentum (up to f-shell) functions. Benchmark calculations on molecules and solids with up to 1536 atoms and 20480 basis functions show up to 25x speedup on an H100 GPU relative to the CPU implementation on a 28-core shared memory node. For a 256-water cluster, the ground-state energy and nuclear gradients can be computed in ~30 seconds on a single H100 GPU. This implementation serves as an open-source foundation for many applications, such as ab initio molecular dynamics and high-throughput calculations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24881 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Implementation of the multigrid Gaussian-Plane-Wave algorithm with GPU acceleration in PySCF Li, Rui Zhang, Xing Sun, Qiming Wang, Yuanheng Yang, Junjie Chan, Garnet Kin-Lic Chemical Physics Materials Science Computational Physics Quantum Physics We introduce a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting (FFTDF) approach for efficient Fock builds and nuclear gradient evaluations within Kohn-Sham density functional theory, as implemented in the GPU4PySCF module of PySCF. Our CUDA kernels employ a grid-based parallelization strategy for contracting Gaussian basis function pairs and achieve up to 80% of the FP64 peak performance on NVIDIA GPUs, with no loss of efficiency for high angular momentum (up to f-shell) functions. Benchmark calculations on molecules and solids with up to 1536 atoms and 20480 basis functions show up to 25x speedup on an H100 GPU relative to the CPU implementation on a 28-core shared memory node. For a 256-water cluster, the ground-state energy and nuclear gradients can be computed in ~30 seconds on a single H100 GPU. This implementation serves as an open-source foundation for many applications, such as ab initio molecular dynamics and high-throughput calculations. |
| title | Implementation of the multigrid Gaussian-Plane-Wave algorithm with GPU acceleration in PySCF |
| topic | Chemical Physics Materials Science Computational Physics Quantum Physics |
| url | https://arxiv.org/abs/2603.24881 |