Dataflow frequency analysis based on whole program paths

Efficient use of machine resources in high-performance computer systems requires highly optimizing compilers with sophisticated analyses. Static analysis often fails to identify frequently executed portions of a program which are the places where optimizations achieve the greatest benefit. This paper introduces a novel data flow frequency analysis framework that computes the frequency with which a data flow fact will hold at some program point based on profiling information. Several approaches which approximate the frequencies based on k-edge profiling have been presented. However, no feasible approach for obtaining the accurate solution exists so far. Recently, efficient techniques for recording whole program paths (WPPs) have been developed. Our approach for computing data flow frequencies results in an accurate solution and utilizes WPPs to obtain the solution in reasonable time. In our experiments we show that the execution time of WPP-based frequency analysis is in case of the SPEC benchmark suite only a fraction of the overall compilation time.