Performance of parallel algorithms for a fingerprint image comparison system

Abstract

This paper addresses the problem of analyzing the performance of parallel algorithms for the training procedure of a neural network based fingerprint image comparison (FIC) system. The target architecture is assumed to be a coarse-grain distributed memory parallel architecture. Two types of parallelism: node parallelism and training set parallelism (TSP) are investigated. These algorithms are implemented on a 32 node CM-5. Theoretical analysis and experimental results comparing the performance of these algorithms are presented.