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Manager–worker‐based model for the parallelization of quantum Monte Carlo on heterogeneous and homogeneous networks
Author(s) -
Feldmann Michael T.,
Cummings Julian C.,
Kent David R.,
Muller Richard P.,
Goddard William A.
Publication year - 2007
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.20836
Subject(s) - computer science , implementation , convergence (economics) , homogeneous , monte carlo method , decorrelation , quantum , parallel computing , algorithm , monte carlo algorithm , parallel algorithm , mathematical optimization , theoretical computer science , mathematics , statistics , physics , combinatorics , quantum mechanics , economics , programming language , economic growth
A manager–worker‐based parallelization algorithm for Quantum Monte Carlo (QMC‐MW) is presented and compared with the pure iterative parallelization algorithm, which is in common use. The new manager–worker algorithm performs automatic load balancing, allowing it to perform near the theoretical maximal speed even on heterogeneous parallel computers. Furthermore, the new algorithm performs as well as the pure iterative algorithm on homogeneous parallel computers. When combined with the dynamic distributable decorrelation algorithm (DDDA) [Feldmann et al., J Comput Chem 28, 2309 (2007)], the new manager–worker algorithm allows QMC calculations to be terminated at a prespecified level of convergence rather than upon a prespecified number of steps (the common practice). This allows a guaranteed level of precision at the least cost. Additionally, we show (by both analytic derivation and experimental verification) that standard QMC implementations are not “perfectly parallel” as is often claimed. © 2007 Wiley Periodicals, Inc. J Comput Chem, 2008