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March 2013 Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning
Robert B. Gramacy, Matt Taddy, Stefan M. Wild
Ann. Appl. Stat. 7(1): 51-80 (March 2013). DOI: 10.1214/12-AOAS590

Abstract

We investigate an application in the automatic tuning of computer codes, an area of research that has come to prominence alongside the recent rise of distributed scientific processing and heterogeneity in high-performance computing environments. Here, the response function is nonlinear and noisy and may not be smooth or stationary. Clearly needed are variable selection, decomposition of influence, and analysis of main and secondary effects for both real-valued and binary inputs and outputs. Our contribution is a novel set of tools for variable selection and sensitivity analysis based on the recently proposed dynamic tree model. We argue that this approach is uniquely well suited to the demands of our motivating example. In illustrations on benchmark data sets, we show that the new techniques are faster and offer richer feature sets than do similar approaches in the static tree and computer experiment literature. We apply the methods in code-tuning optimization, examination of a cold-cache effect, and detection of transformation errors.

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Robert B. Gramacy. Matt Taddy. Stefan M. Wild. "Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning." Ann. Appl. Stat. 7 (1) 51 - 80, March 2013. https://doi.org/10.1214/12-AOAS590

Information

Published: March 2013
First available in Project Euclid: 9 April 2013

zbMATH: 06171263
MathSciNet: MR3086410
Digital Object Identifier: 10.1214/12-AOAS590

Keywords: Bayesian methods , Bayesian regression trees , CART , computer experiments , exploratory data analysis , particle filtering , sensitivity analysis , Variable selection

Rights: Copyright © 2013 Institute of Mathematical Statistics

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Vol.7 • No. 1 • March 2013
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