Performance auditing is an online optimization strategy that empirically measures the effectiveness of an optimization on a particular code region. It has the potential to greatly improve performance and prevent degradations due to compiler optimizations. Performance auditing relies on the ability to obtain sufficiently many timings of the region of code to make statistically valid conclusions. This work extends the state-of-the-art of performance auditing systems by allowing a finer level of granularity for obtaining timings and thus, increases the overall effectiveness of a performance auditing system. The problem solved by our technique is an instance of the general problem of correlating a program’s high-level behavior with its binary instructions, and thus, can have uses beyond a performance auditing system. We present our implementation and evaluation of our technique in a production Java VM.