Abstract
The face of parallel computing has changed in the last few years as high performance clusters of workstations are being used in conjunction with supercomputers to solve demanding computational problems. In order for a user to effectively run an application on both tightly coupled and network based clusters, he must often use different algorithms that are suited to the network available on the computing platform. An application may also be able to effectively utilize a different number of processing nodes with a particular algorithm and processor configuration. It is difficult for a user to determine which set of parameters to select in order to customize the application for an available computing environment. The principal aim of this research is to show that fuzzy logic can be used to select the most efficient algorithm and an optimal number of processors for a parallel application. In this paper we examine three algorithms for image convolution which each have advantages depending on the available architecture and problem size. A fuzzy logic technique is developed which is able to make effective selections, freeing the user from an otherwise daunting task. The fuzzy logic selection system is easy to set up and these results can be extended to additional applications.
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References
R. N. Bracewell. Two-demensional Imaging. Prentice-Hall Inc., 1995.
G. Ficili and D. Panno. Performance Analysis of a Fuzzy System in the Policing of Packetized Voice Sources. In Broadband Communications'96-Global infrastructure for the information age Proceedings of the Internation, pages 211–222, 1996.
R. D. W. G. C. Fox and P. C. Messina. Parallel Computing Works! Morgan Kaufmann Publishers Inc., 1994.
A. Geist, A. Beguelin, J. Dongarra, W. Jiang, R. Manchek, and V. Sunderam. PVM: Parallel Virtual Machine-A User's Guide and Tutorial for Networked Computing. MIT Press, 1994.
R. T. H. T. Nguyen, M. Sugeno and R. R. Yager. Theoretical Aspects of Fuzzy Control. John Wiley and Sons, Inc, 1993.
R. Haralick and L. Shapiro. Computer and Robot Vision. Addison Wesley Publishing Company, 1992.
J. M. Holtzmann. Coping with Broadband Traffic Uncertainties: Statistical Uncertainty, Fuzziness, Neural Networks. In IEEE Workshop on Computer Communications, Dana Pt, California, Oct. 1989.
S. Levialdi. Integrated Technology for Parallel Image Processing. Academic Press, INC., 1985.
Y. Man and I. Gath. Detection and Separation of Ring-Shaped Clusters Using Fuzzy Clustering. IEEE, 1994.
J. M. Schopf. Performance Predication in Production Environments. In University of California, 1997.
C. L. Seitz. Resources in Parallel and Concurrent Systems. ACM Press, 1991.
J. Tenber. Digital Image Processing. Prentice-Hall Inc., 1991.
D. S. R. W. E. Alexander and C. S. G. Jr. Parallel Image Processing with the Block Data Parallel Architecture. Proceeding-of-the-IEEE, July 1996.
S. T. Welstead. Neural Network and Fuzzy Logical Application in C/C++. John Wiley and Sons INC., 1994.
S. Yu. Algorithm Selection For Parallel Image Convolution. Brigham Young University. Master Thesis, 1998.
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© 1999 Springer-Verlag
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Yu, S., Clement, M., Snell, Q., Morse, B. (1999). Parallel algorithm and processor selection based on fuzzy logic. In: Sloot, P., Bubak, M., Hoekstra, A., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1999. Lecture Notes in Computer Science, vol 1593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0100605
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DOI: https://doi.org/10.1007/BFb0100605
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