Accelerating quantum monte carlo simulations of real materials on GPU clusters

Kenneth Esler; Jeongnim Kim; David Ceperley; Luke Shulenburger

doi:10.1109/MCSE.2010.122

Accelerating quantum monte carlo simulations of real materials on GPU clusters

Kenneth Esler, Jeongnim Kim, David Ceperley, Luke Shulenburger

Research output: Contribution to journal › Article › peer-review

Abstract

More accurate than mean-field methods and more scalable than quantum chemical methods, continuum quantum Monte Carlo (QMC) is an invaluable tool for predicting the properties of matter from fundamental principles. Because QMC algorithms offer multiple forms of parallelism, they're ideal candidates for acceleration in the many-core paradigm.

Original language	English (US)
Article number	5601669
Pages (from-to)	40-51
Number of pages	12
Journal	Computing in Science and Engineering
Volume	14
Issue number	1
DOIs	https://doi.org/10.1109/MCSE.2010.122
State	Published - Jan 1 2012

Keywords

Component
graphics processors
Monte Carlo
physics
scientific computing

ASJC Scopus subject areas

Computer Science(all)
Engineering(all)

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