Parallel Image Processing Applications on a Network of Workstations

Abstract Concurrent computing on networks of distributed computers has gained tremendous attention and popularity in recent years. In this paper, we use this computing environment for the development of efficient parallel image convolution applications for grey-level images and binary images. Significant speedup was achieved using different image sizes, kernel sizes, and number of workstations. We also present a performance prediction model that agrees well with our experimental measurements and allows the highest speedup to be predicted from the knowledge of the ratio of the computation time to the communication time. The main limiting factor in our programming environment is the bandwidth of the network. Thus, it seems with emerging high-speed networks such as ATM networks, parallel computing on networks of distributed computers can be a very attractive alternative to traditional parallel computing on SIMD and MIMD multiprocessors in executing computationally intensive applications in general and image processing applications in particular.

[1]  Michael J. Wozny,et al.  Computer graphics in higher education (Panel Session) , 1983, SIGGRAPH.

[2]  Richard W. Hall,et al.  Image processing on augmented mesh-connected parallel computers , 1994 .

[3]  Jorge L. C. Sanz,et al.  SIMD architectures and algorithms for image processing and computer vision , 1989, IEEE Trans. Acoust. Speech Signal Process..

[4]  Sartaj Sahni,et al.  Image Template Matching on MIMD Hypercube Multicomputers , 1990, J. Parallel Distributed Comput..

[5]  G. C. Fox,et al.  Solving Problems on Concurrent Processors , 1988 .

[6]  Kwan-Liu Ma,et al.  Parallel volume visualization on workstations , 1993, Comput. Graph..

[7]  V. K. Prasanna Kumar,et al.  Parallel architectures and algorithms for image understanding , 1991 .

[8]  Edward R. Dougherty,et al.  Morphological methods in image and signal processing , 1988 .

[9]  Larry S. Davis,et al.  Effective use of SIMD parallelism in low- and intermediate-level vision , 1992, Computer.

[10]  G. Jack Lipovski,et al.  Parallel computing - theory and comparisons , 1987 .

[11]  Vaidy S. Sunderam,et al.  PVM: A Framework for Parallel Distributed Computing , 1990, Concurr. Pract. Exp..

[12]  David R. Nadeau,et al.  NetV: an experimental network-based volume visualization system , 1991, Proceeding Visualization '91.

[13]  Charles C. Weems.  Architectural requirements of image understanding with respect to parallel processing , 1991 .

[14]  John R. Kender,et al.  Parallel processing in the DARPA strategic computing vision program , 1991, IEEE Expert.

[15]  B. R. Meijer.  Rules and algorithms for the design of templates for template matching , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[16]  Dana H. Ballard,et al.  Computer Vision , 1982 .

[17]  Hong Yan,et al.  Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition , 1996, Advances in Fuzzy Systems - Applications and Theory.

[18]  Jerome A. Feldman,et al.  Programming Distributed Systems , 1978, ACM Annual Conference.

[19]  Leonard Uhr,et al.  Parallel computer vision , 1987 .

[20]  J. Bell,et al.  Parallel distributed seismic migration , 1992, Future Gener. Comput. Syst..

[21]  Al Geist,et al.  Network-based concurrent computing on the PVM system , 1992, Concurr. Pract. Exp..

[22]  E. Dubois,et al.  Digital picture processing , 1985, Proceedings of the IEEE.

[23]  D J Evans,et al.  Parallel processing , 1986 .

[24]  Sartaj Sahni,et al.  Reconfigurable mesh algorithms for image shrinking, expanding, clustering, and template matching , 1991, [1991] Proceedings. The Fifth International Parallel Processing Symposium.