Selective search of inlining vectors for program optimization
Rosario Cammarota, Arun Kejariwal, Debora Donato, Alexandru Eugen Nicolau, Alexander V. Veidenbaum · 2012
We propose a novel technique to select the inlining options of a compiler - referred to as an inlining vector, for program optimization. The proposed technique trains a machine learning algorithm to model the relation between inlining vectors and performance (completion time). The training set is composed of sample runs of the programs to optimize - that are compiled with a limited number of inlining vectors. Subject to a given compiler, the model evaluates the benefit of inlining combined with other compiler heuristics. The model is subsequently used to select the inlining vector which minimizes the predicted completion time of a program with respect to a given level of optimization.