Parallel algorithms for image histogramming and connected components with an experimental study (extended abstract)

  • Authors:

  • David A. Bader;Joseph JáJá

  • Affiliations:

  • Institute for Advanced Computer Studies and Department of Electrical Engineering, University of Maryland, College Park, MD;Institute for Advanced Computer Studies and Department of Electrical Engineering, University of Maryland, College Park, MD

  • Venue:
  • PPOPP '95 Proceedings of the fifth ACM SIGPLAN symposium on Principles and practice of parallel programming
  • Year:
  • 1995

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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.