| Summary: | 碩士 === 國立臺灣大學 === 數學研究所 === 102 === The automatic performance tuning (auto-tuning) problem emerges in recent scientific computing applications. Usually, most of the applications are computationally intensive so that they rely on the computational power of the advanced computer. To achieve better performance, the performance tuning on related factors plays an important role. However, the architecture of modern computer becomes more and more complicated, so that the automatic performance tuning is indispensable. Meanwhile, the related factors involve various types, e.g. quantitative and qualitative factors. The difficulty here is the mixed types of input factors. We studied several statistical approaches (e.g. Gaussian Process model) to deal with such problems. A framework called surrogate-based tuning procedure is proposed, where the surrogate here means a statistical model of the tuning target. Moreover, our tuning procedure is an consecutive procedure, so an effective consecutive tuning procedure is necessary in this framework. To deal with the mixed input types, we proposed a extended method from the classical expected improvement method which is widely used in global optimization problems. And we compare their performances with many testing examples and real data in scientific computing. Finally, based on our results, we concluded a guideline for model selection in the auto-tuning procedure.
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