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Title: 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:
ORCiD logo [1];  [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
+ Show Author Affiliations
  1. Duke Univ., Durham, NC (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Sciences (CNMS)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (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:
Journal Article: 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.
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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
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Huhn, William P., Lange, Björn, Yu, Victor Wen-zhe, Yoon, Mina, and Blum, Volker. 2020. "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.
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@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},
url = {https://www.osti.gov/biblio/1631232}, journal = {Computer Physics Communications},
issn = {0010-4655},
number = C,
volume = 254,
place = {United States},
year = {Thu Apr 09 00:00:00 EDT 2020},
month = {Thu Apr 09 00:00:00 EDT 2020}
}
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Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1016/j.cpc.2020.107314
Copyright Statement
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Citation Metrics:
Cited by: 27 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: Program flow for a typical electronic structure calculation involving geometry relaxation or molecular dynamics. Shaded boxes indicate steps contributing to the actual computational workload. Yellow shading indicates steps that are subject to real-space GPU acceleration in this work, whereas gray shading indicates steps that are GPU-accelerated only partly,more » not at all, or that are handled by separate software components outside the scope of this work.« less
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    Figures / Tables found in this record:

    Figure 1(p. 6)figure
    Figure 2(p. 12)figure
    Figure 3(p. 15)figure
    Figure 4(p. 17)figure
    Figure 5(p. 20)figure
    Figure 6(p. 23)figure
    Table 1(p. 27)table
    Table 2(p. 29)table
    Table 3(p. 30)table
    Figure 7(p. 31)figure
    Figure 8(p. 32)figure
    Table 4(p. 33)table
    Figure 9(p. 34)figure
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