Parallel Shared-Memory State-Space Exploration in Stochastic Modeling

Stochastic modeling forms the basis for analysis in many areas including biological and economic systems as well as the per formance and reliability modeling of computers and communication net works One common approach is the state space based technique which starting from a high level model uses depth rst search to generate both a description of every possible state of the model and the dynamics of the transitions between them However these state spaces besides being very irregular in structure are subject to a combinatorial explosion and can thus become extremely large In the interest therefore of utilizing both the large memory capacity and the greater computational performance of modern multiprocessors we are interested in implementing parallel algorithms for the generation and solution of these problems In this paper we describe the techniques we use to generate the state space of a stochastic Petri net model using shared memory multiprocessors We describe some of the problems encountered and our solutions in particu lar the use of modi ed B trees as a data structure for the parallel search process We present results obtained from experiments on two di erent shared memory machines