Parallel Algorithms for Image Histogramming and Connected Components with an Experimental Study

David A. Bader
dbader@umiacs.umd.edu

Joseph JáJá
joseph@umiacs.umd.edu

Institute for Advanced Computer Studies, and
Department of Electrical Engineering,
University of Maryland, College Park, MD 20742
Fri Feb 24 10:32:25 EST 1995

Abstract:

This paper presents efficient and portable implementations of two useful primitives in image processing algorithms, histogramming and connected components. Our general framework is a single-address space, distributed memory programming model. We use efficient techniques for distributing and coalescing data as well as efficient combinations of task and data parallelism. Our connected components algorithm uses a novel approach for parallel merging which performs drastically limited updating during iterative steps, and concludes with a total consistency update at the final step. The algorithms have been coded in SPLIT-C and run on a variety of platforms. Our experimental results are consistent with the theoretical analysis and provide the best known execution times for these two primitives, even when compared with machine specific implementations. More efficient implementations of SPLIT-C will likely result in even faster execution times.

Next: Problem Overview

David A. Bader
dbader@umiacs.umd.edu