Performance models of high performance computing (HPC) applications are important. Suitability of a performance model, however, for a particular performance model is a function of both the accuracy and the cost.
In this paper we present a new approach to capture these unknown features to improve the predictive capabilities of the model. This approach uses a systematic model error-correction procedure that uses evolutionary algorithms to find an error correction term to augment the existing model. Four variations of this procedure were investigated and all were shown to produce better results.
Read about the procedures followed below :
http://en.oboulo.com/parellel-performance-modelling-using-a-genetic-programming-based-error-correction-65048.html
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