Efficient performance analysis of asynchronous systems based on periodicity

This paper presents an efficient method for the performance analysis and optimization of asynchronous systems. An asynchronous system is modeled as a marked graph with probabilistic delay distributions. We show that these systems exhibit inherent periodic behaviors. Based on this property, we derive an algorithm to construct the state space of the system through composition and capture the time evolution of the states into a periodic Markov chain. The system is solved for important performance metrics such as the distribution of input arrival time at a component, which is useful for subsequent system optimization, as well as relative component utilization, system latency and throughput. We also present a tool to demonstrate the feasibility of this method. Initial experimental results are promising, showing over three orders of magnitude improvement in runtime and nearly two orders of magnitude decrease in the size of the state space over previously published results. While the focus of this paper is on asynchronous digital systems, our technique can be applied to other concurrent systems that exhibit global asynchronous behavior, such as GALS and embedded systems.