Commercial servers, such as database or application
servers, often attempt to improve performance via multithreading.
Improper multi-threading architectures can incur
contention, limiting performance improvements. Contention
occurs primarily at two levels: (1) blocking on locks
shared between threads at the software level and (2) contending
for physical resources (such as the cpu or disk) at
the hardware level. Given a set of hardware resources and
an application design, there is an optimal number of threads
that maximizes performance. This paper describes a novel
technique we developed to select the optimal number of
threads of a target-tracking application using a simulationbased
Colored Petri Nets (CPNs) model.
This paper makes two contributions to the performance
analysis of multi-threaded applications. First, the paper
presents an approach for calibrating a simulation model using
training set data to reflect actual performance parameters
accurately. Second, the model predictions are validated
empirically against the actual application performance and
the predicted data is used to compute the optimal configuration
of threads in an application to achieve the desired
performance. Our results show that predicting performance
of application thread characteristics is possible and can be
used to optimize performance.