Accelerating the density-functional tight-binding method using graphical processing units

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Vuong, Van-Quan and Cevallos, Caterina and Hourahine, Ben and Aradi, Bálint and Jakowski, Jacek and Irle, Stephan and Camacho, Cristopher (2023) Accelerating the density-functional tight-binding method using graphical processing units. Journal of Chemical Physics, 158 (8). 084802. ISSN 0021-9606 (https://doi.org/10.1063/5.0130797)

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Abstract

Acceleration of the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) was accomplished using the MAGMA linear algebra library. Two major computational bottlenecks of DFTB ground-state calculations were addressed in our implementation: the Hamiltonian matrix diagonalization and the density matrix construction. The code was implemented and benchmarked on two different computer systems: (1) the SUMMIT IBM Power9 supercomputer at the Oak Ridge National Laboratory Leadership Computing Facility (OLCF) with 1 to 6 NVIDIA Volta V100 GPUs per computer node, and (2) an in-house Intel Xeon computer with 1 to 2 NVIDIA Tesla P100 GPUs. The performance and parallel scalability were measured for three molecular models of 1-, 2- and 3-dimensional chemical systems, represented by carbon nanotubes, covalent organic frameworks, and water clusters.

ORCID iDs

Vuong, Van-Quan, Cevallos, Caterina, Hourahine, Ben ORCID logoORCID: https://orcid.org/0000-0002-7667-7101, Aradi, Bálint, Jakowski, Jacek, Irle, Stephan and Camacho, Cristopher;
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