A cluster-based parallel image processing toolkit

Many image processing tasks exhibit a high degree of data locality and parallelism and map quite readily to specialized massively parallel computing hardware. However, as network technologies continue to mature, workstation clusters are becoming a viable and economical parallel computing resource, so it is important to understand how to use these environments for parallel image processing as well. In this paper we discuss our implementation of a parallel image processing software library (the Parallel Image Processing Toolkit). The Toolkit uses a message-passing model of parallelism designed around the Message Passing Interface (MPI) standard. Experimental results are presented to demonstrate the parallel speedup obtained with the Parallel Image Processing Toolkit in a typical workstation cluster over a wide variety of image processing tasks. We also discuss load balancing and the potential for parallelizing portions of image processing tasks that seem to be inherently sequential, such as visualization and data I/O.