Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-clustering tool, P-CLUSTER, designed to execute on a network of workstations. Experiments involving the unsupervised segmentation of standard texture images were performed. For some data sets, a 96 percent reduction in computation was achieved.

Algorithmic enhancements are described that allow large reduction (for some data sets, over 95 percent) in the number of floating point operations in mean square error data clustering. These improvements are incorporated into a parallel data clustering tool, P-CLUSTER, developed in an earlier study. Experiments on segmenting standard texture images show that the proposed enhancements enable clustering of an entire 512/spl times/512 image at approximately the same computational cost as that of previous methods applied to only 5 percent of the image pixels.

Parallel implementations of two computer vision algorithms on distributed cluster platforms are described. The first algorithm is a square-error data clustering method whose parallel implementation is based on the well-known sequential CLUSTER program. The second algorithm is a motion parameter estimation algorithm used to determine correspondence between two images taken of the same scene. Both algorithms have been implemented and tested on cluster platforms using the PVM package. Performance measurements demonstrate that it is possible to attain good performance in terms of execution time and speedup for large-scale problems, provided that adequate memory; swap space, and I/O capacity are available at each node.

This paper addresses the problem of collective data distribution, specifically multicasi, in wormhole-rouied hypercubes.The system model allows a processor to send and receive data in all dimensions simultaneously.New theoretical results that characterize contention among messages in wormhole-routed hypercubes are developed and used to design new multicast routing algorithms.The algorithms are compared in terms of the number of steps required in each, their measured execution times when implemented on a relatively small-scale nCUBE-2, and their simulated execution times on larger hypercubes.The results indicate that significant performance improvement is possible when the multicast algorithm actively identifies and uses multiple ports in parallel.