Manager-worker-based model for the parallelization of Quantum Monte Carlo on heterogeneous and homogeneous networks.

Journal Article · Mon Oct 01 04:00:00 EDT 2007 · Proposed for publication in the Journal of Computational Chemistry.

OSTI ID:946564

Kent, IV, David R ^[1]; Muller, Richard Partain ^[1]; Cummings, Julian C ^[1]; Goddard, III, William A ^[2]; Feldmann, Michael T ^[1]

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1. California Institute of Technology, Pasadena, CA
2. - California Institute of Technology, Pasadena, CA

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.

• Research Organization: Sandia National Laboratories
• Sponsoring Organization: USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 946564
• Report Number(s): SAND2007-6492J
• Journal Information: Proposed for publication in the Journal of Computational Chemistry., Journal Name: Proposed for publication in the Journal of Computational Chemistry.
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
99 GENERAL AND MISCELLANEOUS
ALGORITHMS
LABOR RELATIONS
MANAGEMENT
MONTE CARLO METHOD
NETWORK ANALYSIS
PARALLEL PROCESSING
PERSONNEL