Efficient implementation of the quasiparticle self-consistent $GW$ method on GPU
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910986170007552 |
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| author | Obata, Masao Kotani, Takao Oda, Tatsuki |
| author_facet | Obata, Masao Kotani, Takao Oda, Tatsuki |
| contents | We have developed a multi-GPU version of the quasiparticle self-consistent $GW$ (QSGW), a cutting-edge method for describing electronic excitations in a first-principles approach. While the QSGW calculation algorithm is inherently well-suited for GPU computation due to its reliance on large-scale tensor operations, achieving a maintainable and extensible implementation is not straightforward. Addressing this, we have developed a GPU version within the \texttt{ecalj} package, utilizing module-based programming style in modern Fortran. This design facilitates future development and code sustainability. Following the summary of the QSGW formalism, we present our GPU implementation approach and the results of benchmark calculations for two types of systems to demonstrate the capability of our GPU-supported QSGW calculations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03477 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient implementation of the quasiparticle self-consistent $GW$ method on GPU Obata, Masao Kotani, Takao Oda, Tatsuki Computational Physics Materials Science We have developed a multi-GPU version of the quasiparticle self-consistent $GW$ (QSGW), a cutting-edge method for describing electronic excitations in a first-principles approach. While the QSGW calculation algorithm is inherently well-suited for GPU computation due to its reliance on large-scale tensor operations, achieving a maintainable and extensible implementation is not straightforward. Addressing this, we have developed a GPU version within the \texttt{ecalj} package, utilizing module-based programming style in modern Fortran. This design facilitates future development and code sustainability. Following the summary of the QSGW formalism, we present our GPU implementation approach and the results of benchmark calculations for two types of systems to demonstrate the capability of our GPU-supported QSGW calculations. |
| title | Efficient implementation of the quasiparticle self-consistent $GW$ method on GPU |
| topic | Computational Physics Materials Science |
| url | https://arxiv.org/abs/2506.03477 |