Kohn–Sham time-dependent density functional theory with Tamm–Dancoff approximation on massively parallel GPUs  

作　　者：Inkoo Kim Daun Jeong Won-Joon Son Hyung-Jin Kim Young Min Rhee Yongsik Jung Hyeonho Choi Jinkyu Yim Inkook Jang Dae Sin Kim 

机构地区：[1]Innovation Center,Samsung Electronics,Hwaseong 18448,Republic of Korea [2]Department of Chemistry,Korea Advanced Institute of Science and Technology(KAIST),Daejeon 34141,Republic of Korea [3]Samsung Advanced Institute of Technology,Samsung Electronics,Suwon 16678,Republic of Korea

出　　处：《npj Computational Materials》2023年第1期1556-1567,共12页计算材料学（英文）

基　　金：This work was in part supported by the National Research Foundation(NRF)of Korea(Grant No.2020R1A5A1019141 and 2021R1A2C2094153).Computational resources were provided by the Supercomput-ing Center of Samsung Electronics.

摘　　要：We report a high-performance multi graphics processing unit(GPU)implementation of the Kohn–Sham time-dependent density functional theory(TDDFT)within the Tamm–Dancoff approximation.Our algorithm on massively parallel computing systems using multiple parallel models in tandem scales optimally with material size,considerably reducing the computational wall time.A benchmark TDDFT study was performed on a green fluorescent protein complex composed of 4353 atoms with 40,518 atomic orbitals represented by Gaussian-type functions,demonstrating the effect of distant protein residues on the excitation.As the largest molecule attempted to date to the best of our knowledge,the proposed strategy demonstrated reasonably high efficiencies up to 256 GPUs on a custom-built state-of-the-art GPU computing system with Nvidia A100 GPUs.We believe that our GPU-oriented algorithms,which empower first-principles simulation for very large-scale applications,may render deeper understanding of the molecular basis of material behaviors,eventually revealing new possibilities for breakthrough designs on new material systems.

关 键 词：GPUS GRAPHICS MASSIVE 

分 类 号：O17[理学—数学]