6533b829fe1ef96bd1289879
RESEARCH PRODUCT
Parallelization of adaptive MC integrators
Richard Kreckelsubject
Variable (computer science)Hardware and ArchitectureComputer scienceAdaptive behaviourIntegratorMonte Carlo methodConvergence (routing)FOS: Physical sciencesGeneral Physics and AstronomyParallel computingComputational Physics (physics.comp-ph)Physics - Computational PhysicsNumerical integrationdescription
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-parallelization. A portable implementation of this semi-micro parallelization is used in the xloops-project and is made publicly available.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 1997-10-21 | Computer Physics Communications |