  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.
