Accelerating the density-functional tight-binding method using graphical processing units
| Autor: | Van-Quan Vuong, Caterina Cevallos, Ben Hourahine, Bálint Aradi, Jacek Jakowski, Stephan Irle, Cristopher Camacho |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | The Journal of Chemical Physics. 158:084802 |
| ISSN: | 1089-7690 0021-9606 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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