Parallel image processing applications on a network of workstations

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 spe...

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Bibliographic Details
Published in:Parallel computing Vol. 21; no. 1; pp. 137 - 160
Main Authors: Lee, Chi-kin, Hamdi, Mounir
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 1995
Elsevier
Subjects:
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Convolution algorithm
Distributed memory multiprocessor
Exact sciences and technology
EXPRESS
Image processing
Network of workstations
Pattern recognition. Digital image processing. Computational geometry
Network of workstations
Image processing
Distributed memory multiprocessor
EXPRESS
Convolution algorithm
Multiprocessor
Workstation
Computation time
Convolution
Grey level image
Prediction
Binary image
Parallel processing
Algorithm
Communication
ISSN:0167-8191, 1872-7336
Online Access:Get full text
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Summary: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.
ISSN:0167-8191
1872-7336
DOI:10.1016/0167-8191(94)00068-L