GPU acceleration of all-electron electronic structure theory using localized numeric atom-centered basis functions
Abstract
We present an implementation of all-electron density-functional theory for massively parallel GPU-based platforms, using localized atom-centered basis functions and real-space integration grids. Special attention is paid to domain decomposition of the problem on non-uniform grids, which enables compute- and memory-parallel execution across thousands of nodes for real-space operations, e.g. the update of the electron density, the integration of the real-space Hamiltonian matrix, and calculation of Pulay forces. To assess the performance of our GPU implementation, we performed benchmarks on three different architectures using a 103-material test set. We find that operations which rely on dense serial linear algebra show dramatic speedups from GPU acceleration: in particular, SCF iterations including force and stress calculations exhibit speedups ranging from 4.5 to 6.6. For the architectures and problem types investigated here, this translates to an expected overall speedup between 3–4 for the entire calculation (including non-GPU accelerated parts), for problems featuring several tens to hundreds of atoms. Additional calculations for a 375-atom Bi2Se3 bilayer show that the present GPU strategy scales for large-scale distributed-parallel simulations.
- Authors:
-
- Duke Univ., Durham, NC (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1631232
- Alternate Identifier(s):
- OSTI ID: 1619314
- Grant/Contract Number:
- AC05-00OR22725; AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computer Physics Communications
- Additional Journal Information:
- Journal Volume: 254; Journal Issue: C; Journal ID: ISSN 0010-4655
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; GPU acceleration; High performance computing; Electronic structure; Density functional theory; Localized basis sets; Domain decomposition
Citation Formats
Huhn, William P., Lange, Björn, Yu, Victor Wen-zhe, Yoon, Mina, and Blum, Volker. GPU acceleration of all-electron electronic structure theory using localized numeric atom-centered basis functions. United States: N. p., 2020.
Web. doi:10.1016/j.cpc.2020.107314.
Huhn, William P., Lange, Björn, Yu, Victor Wen-zhe, Yoon, Mina, & Blum, Volker. GPU acceleration of all-electron electronic structure theory using localized numeric atom-centered basis functions. United States. https://doi.org/10.1016/j.cpc.2020.107314
Huhn, William P., Lange, Björn, Yu, Victor Wen-zhe, Yoon, Mina, and Blum, Volker. Thu .
"GPU acceleration of all-electron electronic structure theory using localized numeric atom-centered basis functions". United States. https://doi.org/10.1016/j.cpc.2020.107314. https://www.osti.gov/servlets/purl/1631232.
@article{osti_1631232,
title = {GPU acceleration of all-electron electronic structure theory using localized numeric atom-centered basis functions},
author = {Huhn, William P. and Lange, Björn and Yu, Victor Wen-zhe and Yoon, Mina and Blum, Volker},
abstractNote = {We present an implementation of all-electron density-functional theory for massively parallel GPU-based platforms, using localized atom-centered basis functions and real-space integration grids. Special attention is paid to domain decomposition of the problem on non-uniform grids, which enables compute- and memory-parallel execution across thousands of nodes for real-space operations, e.g. the update of the electron density, the integration of the real-space Hamiltonian matrix, and calculation of Pulay forces. To assess the performance of our GPU implementation, we performed benchmarks on three different architectures using a 103-material test set. We find that operations which rely on dense serial linear algebra show dramatic speedups from GPU acceleration: in particular, SCF iterations including force and stress calculations exhibit speedups ranging from 4.5 to 6.6. For the architectures and problem types investigated here, this translates to an expected overall speedup between 3–4 for the entire calculation (including non-GPU accelerated parts), for problems featuring several tens to hundreds of atoms. Additional calculations for a 375-atom Bi2Se3 bilayer show that the present GPU strategy scales for large-scale distributed-parallel simulations.},
doi = {10.1016/j.cpc.2020.107314},
journal = {Computer Physics Communications},
number = C,
volume = 254,
place = {United States},
year = {2020},
month = {4}
}
Web of Science
Figures / Tables:

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