We present a distributed approach for the steady state solution of large Markov models. We use asynchronous iterations to minimise processor idle time and graph partitioning techniques to minimise inter-processor communication. We demonstrate the scalability of our approach by solving a benchmark model for a number of large state space sizes on both a network of commodity PCs and a distributed memory parallel computer. The performance of our approach is contrasted with published results for an out-of-core solver.
Information from pubs.doc.ic.ac.uk/async-ukpew.