Accurate Critical Path Analysis via Random Trace Construction

We present a new approach to performing program analysis through profile-guided random generation of instruction traces. Using hardware support available in commercial processors, we profile the behavior of individual instructions. Then, in conjunction with the program binary, we use that information to fabricate short (1,000-instruction) traces by randomly evaluating branches in proportion to their profiled behavior. We demonstrate our technique in the context of critical path analysis, showing it can achieve the same accuracy as a hardware critical path predictor, but with lower hardware requirements. Key to achieving this accuracy is correctly identifying memory dependences in the fabricated trace, for which purpose we use a form of abstract interpretation to identify aliasing store-load pairs withoutinterpretation to identify aliasing store-load pairs without explicitly profiling them. We also demonstrate that our approach is very tolerant of the quality of profile information available.