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Abstract: Many applications in the area of scientific computing use algorithms with a two-level parallelism based on potential method parallelism and potential system parallelism. In this paper, we investigate the efficient implementation of such algorithms on distributed memory machines. We consider parallel specifications consisting of an upper level of multiprocessor tasks each of which having an internal structure of uni-processor tasks. To achieve an optimal parallel execution time, the parallel... (Update)
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.... be used as heuristic method, which assigns the modules one after another to the processor group with the minimal global execution time [18]. This algorithm has been shown to have a worst case suboptimality bound 4=3 [10] The result of the scheduling step is a global MDG...
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BibTeX entry: (Update)
T. Rauber and G. Runger. Scheduling of Multiprocessor Tasks for Numerical Applications. In Proceedings of the 8th IEEE Symposium on Parallel and Distributed Processing, IEEE, 1996. http://citeseer.ist.psu.edu/rauber96scheduling.html More
@inproceedings{ rauberscheduling,
author = "T. Rauber and G. R{\"u}nger",
title = "Scheduling of Multiprocessor Tasks for Numerical Applications",
pages = "474--481",
url = "citeseer.ist.psu.edu/rauber96scheduling.html" }
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