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Accelerating the density-functional tight-binding method using graphical processing units
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DOI：10.1063/5.0130797.png)
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摘要
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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.
Keyword:
DIVIDE-AND-CONQUER
CHEMISTRY
CLUSTERS
PARALLEL
IMPLEMENTATION
SIMULATIONS
EIGENSOLVER
MOLECULES
EXPANSION
EXTENSION
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
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