Accelerating Quantum Monte Carlo Simulations of Real Materials on GPU Clusters

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 candida...

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Bibliographic Details
Published in:Computing in science & engineering 2012-01, Vol.14 (1), p.40-51
Main Authors: Esler, Kenneth, Kim, Jeongnim, Ceperley, David, Shulenburger, Luke
Format: Article
Language:English
Subjects:
Algorithms
Clusters
Component
Computation
Computational modeling
Computer Science
Computer simulation
Continuums
Graphics processing unit
graphics processors
Mathematical model
Monte Carlo
Monte Carlo methods
physics
Quantum chemistry
Quantum methods
scientific computing
Wave functions
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Summary: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.
ISSN:1521-9615
1558-366X
DOI:10.1109/MCSE.2010.122