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Abstract : |
In this paper we define a knowledge discovery in databases (KDD) methodology to automatically generate metadata (i.e., knowledge rules) from software/machine pair performance databases. This metadata can be used to characterize the computational behavior of various classes of software or machines. The core and the most computationally intensive part of the KDD methodology is the data mining phase which identifies "interesting " patterns from the performance data. The discovery patterns are expressed in a high level representation to be used to summarize and predict the computational behavior of the targeted software/machine. This paper presents an implementation and evaluation of the proposed KDD process for a class of scientific software together with three data mining algorithms (ID3, HOODG, and CN2). For this case study we have selected a set of software that implements the "mesh/grid partitioning " phase of the domain decomposition approach used for the parallel processing of partial differential equation (PDEs) computations. The raw performance database is generated from a population of elliptic, |
