The Research of Parallel Gibbs Simulating Algorithm in Causality Diagram
Chengliang Wang · Jisuanji fangzhen · 2004
The introduction of Gibbs simulating algorithm, which is based on Markov Chain Monte Carlo(MCMC) theorem, greatly improved the reasoning speed of causality diagram methodology. However, there is a way to speed up the simulation of variables with Markov structure, that is to exploit the neighbor structure to enable updates of several components independently. After analyzing the Gibbs algorithm in Causality Diagram, this paper put forward a rule which regulates the process of mapping from reasoning calculation to multiprocessor system, and avoids the waste of calculating resources due to the hidebound processor allotting. This algorithm will flexibly allot the calculating resources according to the processor number and different calculating capability, so the parallel calculating performance could be improved. The validity of this algorithm has been proved through a simulating experiment.