On the determination of inlining vectors for program optimization

In this paper we propose a new technique and a framework to select inlining heuristic constraints - referred to as an inlining vector, for program optimization. The proposed technique uses machine learning to model the correspondence between inlining vectors and performance (completion time). The automatic selection of a machine learning algorithm to build such a model is part of our technique and we present a rigorous selection procedure. Subject to a given architecture, such a model evaluates the benefit of inlining combined with other global optimizations and selects an inlining vector that, in the limits of the model, minimizes the completion time of a program. We conducted our experiments using the GNU GCC compiler and optimized 22 combinations (program, input) from SPEC CINT2006 on the state-of-the-art Intel Xeon Westmere architecture. Compared with optimization level, i.e., -O3, our technique yields performance improvements ranging from 2% to 9%.