Distributed state space generation of discrete-state stochastic models

Gianfranco Ciardo, Joshua Gluckman, David Nicol

Research output: Contribution to journalArticlepeer-review


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 that 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 find effective methods for on-the-fly partitioning, so that the state space is evenly distributed among processors. In this article 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 multicomputer.

Original languageEnglish (US)
Pages (from-to)82-93
Number of pages12
JournalINFORMS Journal on Computing
Issue number1
StatePublished - 1998
Externally publishedYes


  • Graphs
  • Heuristics
  • Networks

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


Dive into the research topics of 'Distributed state space generation of discrete-state stochastic models'. Together they form a unique fingerprint.

Cite this