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A Knowledge Discovery Methodology for Behavior Analysis of Large-Scale Applications on Parallel Architectures

  • Elias N. Houstis
  • Vassilios S. Verykios
  • Ann C. Catlin
  • John R. Rice
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2660)

Abstract

The focus of this paper is the application and extension of the knowledge discovery in databases process [5] developed in PYTHIA recommender system, to analyze the behavior of a DOE ASCI application/hardware pairs in the context of POEMS project[4]. The POEMS project has built a library of models for modeling scalable architectures like those in the ASCI program. Moreover, it supports detail simulation of a variety of state-of-the-art processors and memory hierarchies and incorporates parallel evaluation of discrete-event simulation. The driver application used is SWEEP3D.

Keywords

Recommender System Data Mining Knowledge Discovery Scientific Computing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Elias N. Houstis
    • 1
    • 2
  • Vassilios S. Verykios
    • 1
    • 3
  • Ann C. Catlin
    • 2
  • John R. Rice
    • 2
  1. 1.Dept. of Computer and Commun. Engr.Univ. of ThessalyVolosGREECE
  2. 2.Dept. of Computer SciencesPurdue UniversityWest LafayetteUSA
  3. 3.Research and Academic Computer Technology InstitutePatrasGREECE

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