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ACM Transactions on Architecture and Code Optimization (TACO)
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Feedback-driven program optimization (FDO) is common in modern compilers, including Just-In-Time compilers increasingly adopted for object-oriented or scripting languages. This paper describes a systematic study in understanding and alleviating the effects of sampling errors on the usefulness of the obtained profiles for FDO. Taking a statistical approach, it offers a series of counter-intuitive findings, and identifies two kinds of profile errors that affect FDO critically, namely zero-count errors and inconsistency errors. It further proposes statistical profile rectification, a simple approach to correcting profiling errors by leveraging statistical patterns in a profile. Experiments show that the simple approach enhances the effectiveness of sampled profile-based FDO dramatically, increasing the average FDO speedup from 1.16X to 1.3X, around 92% of what full profiles can yield.