GPU-Accelerated Large-Scale Excited-State Simulation Based on Divide-and-Conquer Time-Dependent Density-Functional Tight-Binding

Takeshi Yoshikawa, Nana Komoto, Yoshifumi Nishimura, Hiromi Nakai

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The present study implemented the divide-and-conquer time-dependent density-functional tight-binding (DC-TDDFTB) code on a graphical processing unit (GPU). The DC method, which is a linear-scaling scheme, divides a total system into several fragments. By separately solving local equations in individual fragments, the DC method could reduce slow central processing unit (CPU)-GPU memory access, as well as computational cost, and avoid shortfalls of GPU memory. Numerical applications confirmed that the present code on GPU significantly accelerated the TDDFTB calculations, while maintaining accuracy. Furthermore, the DC-TDDFTB simulation of 2-acetylindan-1,3-dione displays excited-state intramolecular proton transfer and provides reasonable absorption and fluorescence energies with the corresponding experimental values.

Original languageEnglish
Pages (from-to)2778-2786
Number of pages9
JournalJournal of Computational Chemistry
Volume40
Issue number31
DOIs
Publication statusPublished - 2019 Dec 5

Keywords

  • divide-and-conquer method
  • excited-state theory
  • graphical processor unit
  • linear scaling
  • time-dependent density-functional tight-binding method

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

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