Parallel image processing applications on a network of workstations

Concurrent computing on networks of distributed computers has gained tremendous attention and popularity in recent years. In this paper, we use this computing environment for the development of efficient parallel image convolution applications for grey-level images and binary images. Significant speedup was achieved using different image sizes, kernel sizes, and number of workstations. We also present a performance prediction model that agrees well with our experimental measurements and allows the highest speedup to be predicted from the knowledge of the ratio of the computation time to the communication time. The main limiting factor in our programming environment is the bandwidth of the network. Thus, it seems with emerging high-speed networks such as ATM networks, parallel computing on networks of distributed computers can be a very attractive alternative to traditional parallel computing on SIMD and MIMD multiprocessors in executing computationally intensive applications in general and image processing applications in particular.