Microarchitecture Sensitive Empirical Models for Compiler Optimizations

This paper proposes the use of empirical modeling techniques for building microarchitecture sensitive models for compiler optimizations. The models we build relate program performance to settings of compiler optimization flags, associated heuristics and key microarchitectural parameters. Unlike traditional analytical modeling methods, this relationship is learned entirely from data obtained by measuring performance at a small number of carefully selected compiler/microarchitecture configurations. We evaluate three different learning techniques in this context viz. linear regression, adaptive regression splines and radial basis function networks. We use the generated models to a) predict program performance at arbitrary compiler/microarchitecture configurations, b) quantify the significance of complex interactions between optimizations and the microarchitecture, and c) efficiently search for ’optimal’ settings of optimization flags and heuristics for any given microarchitectu...

• Type: Conference
• Year: 2007
• Where: CGO
• Authors: Kapil Vaswani, Matthew J. Thazhuthaveetil, Y. N. Srikant, P. J. Joseph
• Keywords: CGO 2007, Compiler Optimization, Microarchitecture Sensitive Models, Program Performance, Software Engineering