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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Houstis, E. N., Verykios, V. S., Catlin, A. C., & Rice, J. R. (2003). A knowledge discovery methodology for behavior analysis of large-scale applications on parallel architectures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2660, 739–748. https://doi.org/10.1007/3-540-44864-0_76
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