Title: The cost of eigenvalue computation on distributed-memory MIMD multiprocessors
Abstract: In [20], Simon proves that bisection is not the optimal method for computing an eigenvalue on a single vector processor. In this paper, we show that his analysis does not extend in a straightforward way to the computation of an eigenvalue on a distributed-memory MIMD multiprocessor. In particular, we show how the optimal number of sections (and processors) to use for multisection depends on variables such as the matrix size and the ratio of communication and computation costs. We also show that parallel multisection outperforms the variant of parallel bisection called polysection proposed by Swarztrauber in [22] for this problem on a distributed-memory MIMD multiprocessor. We present the results of experiments on the 64-processor Intel iPSC/2 hypercube and the 512-processor Intel Touchstone Delta mesh multiprocessor.
Publication Year: 1995
Publication Date: 1995-03-01
Language: en
Type: article
Indexed In: ['crossref']
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