State space construction and steady-state solution of GSPNs on a shared-memory multiprocessor

A common approach for the quantitative analysis of a generalized stochastic Petri net GSPN is to gener ate its entire state space and then solve the correspond ing continuous time Markov chain CTMC numeri cally This analysis often su ers from two major prob lems the state space explosion and the sti ness of the CTMC In this paper we present parallel algorithms for shared memory machines that attempt to allevi ate both of these di culties the large main memory capacity of a multiprocessor can be utilized and long computation times are reduced by e cient paralleliza tion The algorithms comprise both CTMC construc tion and numerical steady state solution We give ex perimental results obtained with a Convex SPP shared memory multiprocessor that show the behavior of the algorithms and the parallel speedups obtained