A Pattern Recognition Approach for Speculative Firing Prediction in Distributed Saturation State-Space Generation

Abstract

The saturation strategy for symbolic state-space generation is particularly effective for globally-asynchronous locally-synchronous systems. A distributed version of saturation, SaturationNOW, uses the overall memory available on a network of workstations to effectively spread the memory load, but its execution is essentially sequential. To achieve true parallelism, we explore a speculative firing prediction, where idle workstations work on predicted future event firing requests. A naïve approach where all possible firings may be explored a priori, given enough idle time, can result in excessive memory requirements. Thus, we introduce a history-based approach for firing prediction that recognizes firing patterns and explores only firings conforming to these patterns. Experiments show that our heuristic improves the runtime and has a small memory overhead.