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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Classifying Execution Times in Parallel Computing Systems: A Classical Hypothesis Testing Approach
verfasst von : Hugo Pacheco, Jonathan Pino, Julio Santana, Pablo Ulloa, Jorge E. Pezoa
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer Berlin Heidelberg
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In this paper two classifiers have been derived in order to determine if identical computer tasks have been executed at different processors. The classifiers have been developed analytically following a classical hypothesis testing approach. The main assumption of this work is that the probability distribution function (pdf) of the random times taken by the processors to serve tasks are known. This assumption has been fulfilled by empirically characterizing the pdf of such random times. The performance of the classifiers developed here has been assessed using traces from real processors. Further, the performance of the classifiers is compared to heuristic classifiers, linear discriminants, and non-linear discriminants among other classifiers.