Independent performance modeling of parallel architectures and algorithms

A key requirement for the effective use of multiprocessor systems in real-world applications is an ability to accurately predict the performance of a specific algorithm on a specific architecture. Such performance prediction tools assist the system designer in initially selecting, and then modifying, both the algorithm and the architecture to obtain acceptable performance. In this paper, we present a modeling approach that permits separate evaluation of algorithm and architecture performance with only a small number of "cross" parameters required to link the two models. An example application of this technique to a Gaussian elimination algorithm on two dissimilar multiprocessor architectures shows good agreement with actual performance figures obtained from measurement and simulation.<
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