Analysis of large GSPN models: a distributed solution tool

Distributed computing systems, making available large memories and powerful computational resources allow the analysis of problems of unprecedented complexity. The exploitation of the potential offered by these systems highly depends upon the availability of efficient tools devoted to the solution of specific problems. The paper describes a distributed approach to the analysis of large GSPN models whose applicability to real systems was often constrained by the explosion in the size of the underlying state space representation. A performing algorithm for distributed construction of large state space graphs generated by GSPN models is described in detail. The subsequent distributed numerical solution of the Markov chain is then presented and discussed, along with its current limitations. The implementation of the complete distributed solution tool is integrated with the well known GreatSPN package. The use of the widespread MPI Message Passing library makes the tool portable on a large class of distributed and parallel architectures. Performance results in the case of nets with large stale space cardinalities are discussed, considering an experimental low-cost, general purpose PC cluster