High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models often requires the generation and storage of the entire underlying state space. This imposes practical limitations on the types of systems which can be modeled. Because of the vast amount of memory consumed, we investigate distributed algorithms for the generation of state space graphs. The distributed construction allows us to take advantage of the combined memory readily available on a network of workstations. The key technical problem is to nd e ective methods for on-the-y partitioning, so that the state space is evenly distributed among processors. In this paper we report on the implementation of a distributed state space generator that may be linked to a number of existing system modeling tools. We discuss partitioning strategies in the context of Petri net models, and report on performance observed on a network of workstations, as well as on a distributed memory multi-computer.
The classical approach of state space partitioning in the setting of Petri Nets dates back to at least the work of Ciardo et al. REF .
Distributed State-Space Generation of Discrete-State Stochastic Models
INFORMS Journal of Computing
Computer Science