| Autor: |
Kim I; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea.; Department of Chemistry, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, United States., Jeong D; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea., Weisburn LP; Department of Chemistry, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, United States., Alexiu A; Department of Chemistry, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, United States., Van Voorhis T; Department of Chemistry, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, United States., Rhee YM; Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea., Son WJ; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea., Kim HJ; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea., Yim J; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea., Kim S; Samsung Advanced Institute of Technology, Samsung Electronics, Suwon 16678, Republic of Korea., Cho Y; Samsung Advanced Institute of Technology, Samsung Electronics, Suwon 16678, Republic of Korea., Jang I; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea., Lee S; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea., Kim DS; Innovation Center, Samsung Electronics, Hwaseong 18448, Republic of Korea. |
| Abstrakt: |
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 calculate the TDA-TDDFT gradient of the S 1 state of a full-scale green fluorescent protein with explicit water solvent molecules, totaling 4353 atoms, at the ωB97X/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. |
