<title>Image partitioning on a hypercube machine</title>

Abstract

Some performance measures for partitioning images among hypercube connected processors are presented. The paramount effect of row-major ordering of image bytes is explicitly taken into account. Subimages are split at row boundaries first and downloaded over a spanning binomial tree. Subimage nearest neighbors are mapped to processor neighbors. A theorem which indicates that subimage locality is preserved is given. Practical constraints of a real machine (nCUBE 2) are incorporated. Performance comparisons between this and related image communication techniques are presented.