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

Vuong, Van-Quan; Cevallos, Caterina; Hourahine, Ben; Aradi, Bálint; Jakowski, Jacek; Irle, Stephan; Camacho, Cristopher

doi:10.1063/5.0130797

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

Vuong, Van-Quan·Cevallos, Caterina·Hourahine, Ben·Aradi, Bálint·Jakowski, Jacek·Irle, Stephan·Camacho, Cristopher

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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. Herein 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 with 1–6 NVIDIA Volta V100 GPUs per computer node and (2) an in-house Intel Xeon computer with 1–2 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.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY covalent organic framework density functional theory density-functional tight-binding density-matrix graphics processing units nanotubes supercomputer

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Vuong, Van-Quan, Cevallos, Caterina, Hourahine, Ben, Aradi, Bálint, Jakowski, Jacek, Irle, Stephan, Camacho, Cristopher. 2023-02-24. Accelerating the density-functional tight-binding method using graphical processing units. https://doi.org/10.1063/5.0130797

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