Kohn-Sham Time-Dependent Density Functional Theory on the Massively Parallel Graphics Processing Units

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 newly developed GPU 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 4,353 atoms with 40,518 atomic orbitals represented by Gaussian-type functions. 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.