Kinetic energy classification and smoothing for compact Bspline basis sets in quantum Monte Carlo
Abstract
Quantum Monte Carlo calculations of defect properties of transition metal oxides have become feasible in recent years due to increases in computing power. As the system size has grown, availability of onnode memory has become a limiting factor. Saving memory while minimizing computational cost is now a priority. The main growth in memory demand stems from the Bspline representation of the single particle orbitals, especially for heavier elements such as transition metals where semicore states are present. Despite the associated memory costs, splines are computationally efficient. In this paper, we explore alternatives to reduce the memory usage of splined orbitals without significantly affecting numerical fidelity or computational efficiency. We make use of the kinetic energy operator to both classify and smooth the occupied set of orbitals prior to splining. By using a partitioning scheme based on the perorbital kinetic energy distributions, we show that memory savings of about 50% is possible for select transition metal oxide systems. Finally, for production supercells of practical interest, our scheme incurs a performance penalty of less than 5%.
 Authors:

 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Materials Science and Technology Division
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
 Sponsoring Org.:
 USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC22)
 OSTI Identifier:
 1423013
 Alternate Identifier(s):
 OSTI ID: 1418079
 Grant/Contract Number:
 AC0500OR22725
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Journal of Chemical Physics
 Additional Journal Information:
 Journal Volume: 148; Journal Issue: 4; Journal ID: ISSN 00219606
 Publisher:
 American Institute of Physics (AIP)
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; interpolation; Monte Carlo methods; programming languages; density functional theory; catalysis
Citation Formats
Krogel, Jaron T., and Reboredo, Fernando A. Kinetic energy classification and smoothing for compact Bspline basis sets in quantum Monte Carlo. United States: N. p., 2018.
Web. doi:10.1063/1.4994817.
Krogel, Jaron T., & Reboredo, Fernando A. Kinetic energy classification and smoothing for compact Bspline basis sets in quantum Monte Carlo. United States. doi:10.1063/1.4994817.
Krogel, Jaron T., and Reboredo, Fernando A. Thu .
"Kinetic energy classification and smoothing for compact Bspline basis sets in quantum Monte Carlo". United States. doi:10.1063/1.4994817. https://www.osti.gov/servlets/purl/1423013.
@article{osti_1423013,
title = {Kinetic energy classification and smoothing for compact Bspline basis sets in quantum Monte Carlo},
author = {Krogel, Jaron T. and Reboredo, Fernando A.},
abstractNote = {Quantum Monte Carlo calculations of defect properties of transition metal oxides have become feasible in recent years due to increases in computing power. As the system size has grown, availability of onnode memory has become a limiting factor. Saving memory while minimizing computational cost is now a priority. The main growth in memory demand stems from the Bspline representation of the single particle orbitals, especially for heavier elements such as transition metals where semicore states are present. Despite the associated memory costs, splines are computationally efficient. In this paper, we explore alternatives to reduce the memory usage of splined orbitals without significantly affecting numerical fidelity or computational efficiency. We make use of the kinetic energy operator to both classify and smooth the occupied set of orbitals prior to splining. By using a partitioning scheme based on the perorbital kinetic energy distributions, we show that memory savings of about 50% is possible for select transition metal oxide systems. Finally, for production supercells of practical interest, our scheme incurs a performance penalty of less than 5%.},
doi = {10.1063/1.4994817},
journal = {Journal of Chemical Physics},
number = 4,
volume = 148,
place = {United States},
year = {2018},
month = {1}
}
Web of Science
Figures / Tables:
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