Classifying Execution Times in Parallel Computing Systems: A Classical Hypothesis Testing Approach

  • Hugo Pacheco
  • Jonathan Pino
  • Julio Santana
  • Pablo Ulloa
  • Jorge E. Pezoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)

Abstract

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.

Keywords

Execution Time False Alarm Intrusion Detection Receiver Operating Curve False Alarm Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hugo Pacheco
    • 1
  • Jonathan Pino
    • 1
  • Julio Santana
    • 1
  • Pablo Ulloa
    • 1
  • Jorge E. Pezoa
    • 1
  1. 1.Departamento de Ingeniería Eléctrica and Center for Optics and Photonics (CEFOP)Universidad de ConcepciónConcepciónChile

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