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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.16586 |
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| _version_ | 1866916333754515456 |
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| author | Kim, Inkoo Jeong, Daun Weisburn, Leah Alexiu, Alexandra Van Voorhis, Troy Rhee, Young Min Son, Won-Joon Kim, Hyung-Jin Yim, Jinkyu Kim, Sungmin Cho, Yeonchoo Jang, Inkook Lee, Seungmin Kim, Dae Sin |
| author_facet | Kim, Inkoo Jeong, Daun Weisburn, Leah Alexiu, Alexandra Van Voorhis, Troy Rhee, Young Min Son, Won-Joon Kim, Hyung-Jin Yim, Jinkyu Kim, Sungmin Cho, Yeonchoo Jang, Inkook Lee, Seungmin Kim, Dae Sin |
| contents | Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn-Sham time-dependent density functional theory (TDDFT), employing the Tamm-Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange-correlation functionals within the range-separated scheme. As an illustrative example, we calculated the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the wB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_16586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Very-Large-Scale GPU-Accelerated Nuclear Gradient of Time-Dependent Density Functional Theory with Tamm-Dancoff Approximation and Range-Separated Hybrid Functionals Kim, Inkoo Jeong, Daun Weisburn, Leah Alexiu, Alexandra Van Voorhis, Troy Rhee, Young Min Son, Won-Joon Kim, Hyung-Jin Yim, Jinkyu Kim, Sungmin Cho, Yeonchoo Jang, Inkook Lee, Seungmin Kim, Dae Sin Chemical Physics Modern graphics processing units (GPUs) provide an unprecedented level of computing power. In this study, we present a high-performance, multi-GPU implementation of the analytical nuclear gradient for Kohn-Sham time-dependent density functional theory (TDDFT), employing the Tamm-Dancoff approximation (TDA) and Gaussian-type atomic orbitals as basis functions. We discuss GPU-efficient algorithms for the derivatives of electron repulsion integrals and exchange-correlation functionals within the range-separated scheme. As an illustrative example, we calculated the TDA-TDDFT gradient of the S1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the wB97X/def2-SVP level of theory. Our algorithm demonstrates favorable parallel efficiencies on a high-speed distributed system equipped with 256 Nvidia A100 GPUs, achieving >70% with up to 64 GPUs and 31% with 256 GPUs, effectively leveraging the capabilities of modern high-performance computing systems. |
| title | Very-Large-Scale GPU-Accelerated Nuclear Gradient of Time-Dependent Density Functional Theory with Tamm-Dancoff Approximation and Range-Separated Hybrid Functionals |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2407.16586 |