In: Proceedings of the Seventh International Workshop on Petri Nets and Performance Models, June 3-6, 1997, Saint Malo, France, pages 132-141. Los Alamitos, California: IEEE Computer Society, June 1997.
Abstract: Use of a high-level modeling representation, such as stochastic Petri nets, frequently results in a very large state space. In this paper, we propose new methods that can tolerate such large state spaces and that do not require any special structure in the model. First, we develop methods that generate rows and columns of the state transition-rate-matrix on-the-fly, eliminating the need to explicitly store the matrix at all. Next, we introduce a new iterative solution method, called modified adaptive Gauss-Seidel, that exhibits locality in its use of data from the state transition-rate-matrix. This permits the caching of portions of the matrix, hence reducing the solution time. Finally, we develop a new memory- and computationally-efficient technique for Gauss-Seidel-based solvers that avoids the need for generating rows of A in order to solve Ax=b. Taken together, these new results show that one can solve very large SPN, GSPN, SRN, and SAN models without any special structure.