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Performance of scientific processing in networks of workstations: matrix multiplication example
Keywords:
performance, cluster parallel computing, scientific processingAbstract
Parallel computing on networks of workstations are intensively used in some application areas such as linear algebra operations. Topics such as processing as well as communication hardware heterogeneity are considered solved by the use of parallel processing libraries, but experimentation about performance under these circumstances seems to be necessary. Also, installed networks of workstations are specially attractive due to its extremely low cost for parallel processing as well as its great availability given the number of installed local area networks. The performance of such networks of workstations is fully analyzed by means of a simple application: matrix multiplication. A parallel algorithm is proposed for matrix multiplication derived from two main sources: a) previous proposed algorithms for this task in traditional parallel computers, and b) the bus based interconnection network of workstations. This parallel algorithm is analyzed experimentally in terms of workstations workload and data communication, two main factors in overall parallel computing performance.
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References
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[3] Blackford L., J. Choi, A. Cleary, E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, R. Whaley, ScaLAPACK Users' Guide, SIAM, Philadelphia, 1997.
[4] Cannon L. E., A Cellular Computer to Implement the Kalman Filter Algorithm, Ph.D. Thesis, Montana State University, Bozman, Montana, 1969.
[5] Choi J., J. J. Dongarra, D. W. Walker, PUMMA: Parallel Universal Matrix Multiplication Algorithms on Distributed Memory Concurrent Computers, in Concurrency: Practice and Experience, 6:543-570, 1994.
[6] Choi J., “A New Parallel Matrix Multiplication Algorithm on Distributed-Memory Concurrent Computers”, Proceedings of the High-Performance Computing on the Information Superhighway, IEEE, HPC-Asia '97.
[7] Dongarra J., A. Geist, R. Manchek, V. Sunderam, Integrated pvm framework supports heterogeneous network computing, Computers in Physics, (7)2, pp. 166-175, April 1993.
[8] Fox G., M. Johnson, G. Lyzenga, S. Otto, J. Salmon, and D. Walker, Solving Problems on Concurrent Processors, Vol. I, Prentice Hall, Englewood Cliffs, New Jersey, 1988.
[9] Golub G. H., C. F. Van Loan, Matrix Computation, Second Edition, The John Hopkins University Press, Baltimore, Maryland, 1989.
[10] Message Passing Interface Forum, MPI: A Message Passing Interface standard, International Journal of Supercomputer Applications, Volume 8 (3/4), 1994.
[11] Strassen V., Gaussian Elimination Is Not Optimal, Numerische Mathematik, Vol. 13, 1969.
[12] Tinetti F., A. Quijano, A. De Giusti, Heterogeneous Networks of Workstations and SPMD Scientific Computing, 1999 International Conference on Parallel Processing, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan, September 21 - 24, 1999.
[13] van de Geijn R., J. Watts, SUMMA Scalable Universal Matrix Multiplication Algorithm, LAPACK Working Note 99, Technical Report CS-95-286, University of Tenesse, 1995.
[14] Whaley R., J. Dongarra, Automatically Tuned Linear Algebra Software, Proceedings of the SC98 Conference, Orlando, FL, IEEE Publications, November, 1998.
[15] Wilkinson B., Allen M., Parallel Programming: Techniques and Applications Using Networking Workstations, Prentice-Hall, Inc., 1999.
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Published
2001-05-01
How to Cite
Tinetti, F. G. (2001). Performance of scientific processing in networks of workstations: matrix multiplication example. Journal of Computer Science and Technology, 1(04), 10 p. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/998
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