Feedback-directed specialization of code

Based on feedback information, a large number of optimizations can be performed by the compiler. This information actually indicates the changing behavior of the applications and can be used to specialize code accordingly.Code specialization is a way to facilitate the compiler to perform optimizations by providing the information regarding variables in the code. It is however difficult to select the variables which maximize the benefit of specialization. Also the overhead of specialization and code size increase are the main issues while specializing code.This paper suggests a novel method for improving the performance using specialization based on feedback information and analysis. The code is iteratively specialized after selecting candidate variables by using a heuristic, followed by generation of optimized templates. These templates require a limited set of instructions to be specialized at runtime and are valid for a large number of values. The overhead of runtime specialization is further minimized through optimal software cache of template clones whose instantiation can be performed at static compile time.The experiments have been performed on Itanium-II(IA-64) and Pentium-IV processors using icc and gcc compilers. A significant improvement in terms of execution speed and reduction of code size has been achieved for SPEC and FFTW benchmarks.