Hybrid algorithms in quantum Monte Carlo

Jeongnim Kim, Kenneth P. Esler, Jeremy McMinis, Miguel A. Morales, Bryan K. Clark, Luke Shulenburger, David M. Ceperley

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Article number012008
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - 2012
Event23rd Conference on Computational Physics, CCP 2011 - Gatlinburg, TN, United States
Duration: Oct 30 2012Nov 3 2012

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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