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QMCPACK v3.15.0

Paul R. C. Kent, Gani Annaberdiyev, Thomas Applencourt, Anouar Benali, M. Chandler Bennett, Ray Clay, Alfredo A. Correa, Mark Dewing, Peter Doak, Amanda Dumi, Kevin E. Gasperich, William F. Godoy, Steven Hahn, Jeongnim Kim, Jaron T. Krogel, Ilkka Kylänpää, Graham Lopez, Ye Luo, Fionn D. Malone, Cody Melton, Morales, Miguel, Nichols A. Romero, Kayahan Saritas, Hyeondeok Shin, Luke Shulenburger, Juha Tiihonen, Joshua Townsend, Shiv Upadhyay, Yubo Yang, Andrea Zen, Luning Zhao

Published Jan 1, 2022• 0 citations
Publisher

Abstract

QMCPACK is an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support.
DOI
10.11578/dc.20221216.1

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QMCPACK is an open source quantum Monte Carlo package for ab initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wavefunctions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary-field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit and graphical processing unit systems. We detail the program's capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://qmcpack.org.
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With advances in algorithms and growing computing powers, quantum Monte Carlo (QMC) methods have become a leading contender for high accuracy calculations for the electronic structure of realistic systems. The performance gain on recent HPC systems is largely driven by increasing parallelism: the number of compute cores of a SMP and the number of SMPs have been going up, as the Top500 list attests. However, the available memory as well as the communication and memory bandwidth per element has not kept pace with the increasing parallelism. This severely limits the applicability of QMC and the problem size it can handle. OpenMP/MPI hybrid programming provides applications with simple but effective solutions to overcome efficiency and scalability bottlenecks on large-scale clusters based on multi/many-core SMPs. We discuss the design and implementation of hybrid methods in QMCPACK and analyze its performance on current HPC platforms characterized by various memory and communication hierarchies.
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