Improving performance through deep value profiling and specialization with code transformation

Abstract

Specialization of code is used to improve the performance of the applications. However, specialization based on ineffective profiles deteriorates the performance. Existing value profiling algorithms are not yet able to address the issue of code size explosion incurred due to specialization of code. This problem can be mitigated by capturing data through profiling that would be useful for specialization of code with minimum code size. In this article, we present an approach to optimize code through value profiling and specialization with code transformation. The values of the parameters selected through an analysis of code are captured in the intervals which are automatically adapted to dynamic behavior of the application. The code is then specialized based on value profiles. The specialized code contains optimizations and may be converted back to the generalized code through a transformation. This approach facilitates the code to obtain optimizations through specialization with minimum size, and no runtime overhead. Using this approach, the experiments performed on Itanium-II (IA-64) architecture with icc compiler v 9.0 show a significant improvement in the performance of the SPEC 2000 benchmarks.