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

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 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.

Automated summary

We accelerated the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) using the MAGMA linear algebra library. Our implementation addressed two major computational bottlenecks of DFTB ground-state calculations: the Hamiltonian matrix diagonalization and the density matrix construction. The code was tested on the SUMMIT IBM Power9 supercomputer and an in-house Intel Xeon computer, showing good performance and parallel scalability for carbon nanotubes, covalent organic frameworks, and water clusters.