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We investigate an application in the automatic tuning of computer codes, anarea of research that has come to prominence alongside the recent rise ofdistributed scientific processing and heterogeneity in high-performancecomputing environments. Here, the response function is nonlinear and noisy andmay not be smooth or stationary. Clearly needed are variable selection,decomposition of influence, and analysis of main and secondary effects for bothreal-valued and binary inputs and outputs. Our contribution is a novel set oftools for variable selection and sensitivity analysis based on the recentlyproposed dynamic tree model. We argue that this approach is uniquely wellsuited to the demands of our motivating example. In illustrations on benchmarkdata sets, we show that the new techniques are faster and offer richer featuresets than do similar approaches in the static tree and computer experimentliterature. We apply the methods in code-tuning optimization, examination of acold-cache effect, and detection of transformation errors.

Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning