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
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
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.