Connection machine vision-Replicated data structures

Abstract

The problem of efficiently processing small data structures on massively parallel single-instruction multiple-data machines using replication methods is discussed. The problem stems from considerations of both multiresolution vision systems and focus of attention vision systems. A general framework for developing replicated algorithms, based on the four steps of embedding, distribution, decomposition, and collection, is described. A simple example is provided based on computing the histogram of a gray-level image. Replicated chain processing is discussed, and an efficient algorithm for ranking the elements in a chain in log (n) time on a concurrent write parallel random access machine is presented.<>