Efficient image processing applications on a network of workstations

Using a cluster of networked workstations as an inexpensive parallel computational platform is an appealing idea. However, very little is known about modeling their parallel performance since most of the the developed models have been designed with traditional parallel computers in mind. In this paper we model the performance of this computing environment for synchronous parallel iterative algorithms. One specific algorithm of this class that is treated in detail in this paper is the parallel image processing convolution. Our model takes into consideration the communication capability of the network, the computing capabilities of the workstations, and load imbalance among the workstations. It was shown that our models accurately model the performance of synchronous iterative algorithms on a cluster of workstations. Moreover, this model can be used to tune various parameters in the system to enhance its performance.