Very-Large-Scale GPU-Accelerated Nuclear Gradient of Time-Dependent Density Functional Theory with Tamm-Dancoff Approximation and Range-Separated Hybrid Functionals

2024/07/23 by Inkoo Kim, Kim, Inkoo, Daun Jeong +25 · 2 citations
Chemistry · Computer Science · #Advanced NMR Techniques and Applications #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Matrix Theory and Algorithms

paper · pdf · doi:10.48550/arxiv.2407.16586

openalex publication_date 2024/07/23 · openalex created_date 2025/01/05 · openalex updated_date 2026/07/28

Abstract

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.

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