Abstract
In this paper, we introduce a data redistribution algorithm which aims at dynamically balancing the workload of image processing algorithms on distributed memory processors. First we briefly review state-of-the-art techniques for load balancing application-specific algorithms. Then we describe the data redistribution technique, which we term “elastic load balancing” in a general framework. We demonstrate the usefulness of our redistribution strategy by comparing the efficiency obtained with and without the elastic algorithm for a thinning algorithm which aims at extracting the skeleton of a binary image. We report experimental results obtained with a Supernode machine, based upon reconfigurable networks of 32 Transputers [Nic]. We obtain a speedup of up to 28 over the sequential algorithm, using a Mandelbrot set as a test image. Note that the speedup with a static allocation of the picture was limited to 17 with the same test image, due to the load imbalance among the processors.
This work has been supported by the Project C3 of the French Council for Research CNRS, and by the ESPRIT Basic Research Action 3280 “NANA” of the European Economic Community
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© 1992 Springer-Verlag Berlin Heidelberg
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Miguet, S., Robert, Y. (1992). Elastic load-balancing for image processing algorithms. In: Zima, H.P. (eds) Parallel Computation. ACPC 1991. Lecture Notes in Computer Science, vol 591. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55437-8_99
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DOI: https://doi.org/10.1007/3-540-55437-8_99
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