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

Abstract

The present study implemented the divide-and-conquer timedependent 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 excitedstate intramolecular proton transfer and provides reasonable absorption and fluorescence energies with the corresponding experimental values.

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning

“…155 In 2019, Nakai and co-workers implemented a GPU-accelerated DC-TDDFTB method, which could reproduce the experimental absorption and uorescence spectra of 2-acetylindan-1,3-dione in explicit acetonitrile solution. 156 It is not straightforward to precisely compare the accurate and costs of various low scaling QM methods because there usually exist some adjustable parameters for achieving the balance between the accuracy and computational costs. Also, those methods may show different performances for various types of large subsystems.…”

“… 155 In 2019, Nakai and co-workers implemented a GPU-accelerated DC-TDDFTB method, which could reproduce the experimental absorption and fluorescence spectra of 2-acetylindan-1,3-dione in explicit acetonitrile solution. 156 …”

From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape

“…For example, the current V100 GPUs group 32 cores into a warp, and there are 160 warps to a GPU 54,55 . Various existing computational chemistry codes have been adapted 53,[56][57][58][59][60][61][62][63][64][65][66][67] or designed from the outset (e.g., TeraChem) 67,68 to use GPUs.…”

On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters

“…Recently, there has been significant research effort afforded to porting electronic structure software to the GPU (Gordon et al, 2020). In the case of large-scale calculations, much work has gone into the development of massively parallel GPU implementations of methods based on plane wave (Maintz et al, 2011;Wang et al, 2011;Jia et al, 2019), real space (Andrade and Aspuru-Guzik, 2013;Hakala et al, 2013), finite element (Das et al, 2019;Motamarri et al, 2020), and various other discretizations (Genovese et al, 2009;van Schoot and Visscher, 2016;Yoshikawa et al, 2019;Huhn et al, 2020) of the Kohn-Sham equations. In this work, we consider the Gaussian basis set discretization of the Kohn-Sham equations (Pople et al, 1992), which poses a number of challenges for GPU implementations.…”