On the Determination of Inlining Vectors for Program Optimization

• Published in: Compiler Construction pp. 164 - 183
• Main Authors: Cammarota, Rosario, Nicolau, Alexandru, Veidenbaum, Alexander V., Kejariwal, Arun, Donato, Debora, Madhugiri, Mukund
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: Absolute Error; Completion Time; Program Optimization; Regression Algorithm; Support Vector Regression
• ISBN: 3642370500; 9783642370502
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-37051-9_9

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%.