Parallelization of adaptive MC integrators

Published: 01-01-1997| Version 1 | DOI: 10.17632/52bnkzkrd8.1
Contributor:
Richard Kreckel

Description

Abstract Monte Carlo (MC) methods for numerical integration seem to be embarrassingly parallel on first sight. When adaptive schemes are applied in order to enhance convergence however, the seemingly most natural way of replicating the whole job on each processor can potentially ruin the adaptive behaviour. Using the popular VEGAS-Algorithm as an example an economic method of semi-micro parallelization with variable grain-size is presented and contrasted with another straightforward approach of macro-... Title of program: pvegas.c Catalogue Id: ADGU_v1_0 Nature of problem Monte Carlo (MC) methods for numerical integration seem to be embarassingly parallel on first sight. When adaptive schemes are applied in order to enhance convergence however, the seemingly most natural way of replicating the whole job on each processor can potentially ruin the adaptive behaviour. Using the popular VEGAS- Algorithm as an example an economic method of semi-micro parallelization with variable grain-size is presented and contrasted with another straightforward approach of macro-par ... Versions of this program held in the CPC repository in Mendeley Data ADGU_v1_0; pvegas.c; 10.1016/S0010-4655(97)00099-4 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

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