Abstract
This paper introduces combined profiling (CP): a new practical methodology to produce statistically sound combined profiles from multiple runs of a program. Combining profiles is often necessary to properly characterize the behavior of a program to support Feedback-Directed Optimization (FDO). CP models program behaviors over multiple runs by estimating their empirical distributions, providing the inferential power of probability distributions to code transformations. These distributions are build from traditional single-run point profiles; no new profiling infrastructure is required. The small fixed size of this data representation keeps profile sizes, and the computational costs of profile queries, independent of the number of profiles combined. However, when using even a single program run, a CP maintains the information available in the point profile, allowing CP to be used as a drop-in replacement for existing techniques. The quality of the information generated by the CP methodology is evaluated in LLVM using SPEC CPU 2006 benchmarks.