CaltechAUTHORS
Published February 10, 2021 | Version public
Journal Article Metadata-only

Many-Body Quantum Chemistry on Massively Parallel Computers

Creators

  • 1. ROR icon Virginia Tech

Abstract

The deployment of many-body quantum chemistry methods onto massively parallel high-performance computing (HPC) platforms is reviewed. The particular focus is on highly accurate methods that have become popular in predictive description of chemical phenomena, such as the coupled-cluster method. The account of relevant literature is preceded by a discussion of the modern and near-future HPC landscape and the relevant computational traits of the many-body methods, in their canonical and reduced-scaling formulations, that underlie the challenges in their HPC realization.

Additional Information

© 2020 American Chemical Society. Received: January 3, 2020; Published: December 11, 2020. This work was supported by the U.S. National Science Foundation (awards 1550456, 1800348, and 1931347) and by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This work used resources of the OLCF at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC05-00OR22725. This research used resources of the ALCF, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. We also acknowledge Advanced Research Computing at Virginia Tech (www.arc.vt.edu) for providing computational resources and technical support that have contributed to the results reported within this paper. The authors declare no competing financial interest.

Additional details

Identifiers

Eprint ID
107098
Resolver ID
CaltechAUTHORS:20201215-141037525

Funding

NSF
OAC-1550456
NSF
CHE-1800348
NSF
OAC-1931347
Department of Energy (DOE)
17-SC-20-SC
Department of Energy (DOE)
DE-AC05-00OR22725
Department of Energy (DOE)
DE-AC02-06CH11357

Dates

Created
2020-12-15
Created from EPrint's datestamp field
Updated
2021-11-16
Created from EPrint's last_modified field