Predicting program behavior using real or estimated profiles

There is a growing interest in optimizations that depend on or benefit from an execution profile that tells where time is spent. How well does a profile from one run describe the behavior of a different run, and how does this compare with the behavior predicted by static analysis of the program? This paper defines two abstract measures of how well a profile predicts actual behavior. According to these measures, real profiles indeed do better than estimated profiles, usually. A perfect profile from an earlier run with the same data set, however, does better still, sometimes by a factor of two. Unfortunately, using such a profile is unrealistic, and can lead to inflated expectations of a profile-driven optimization.

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