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. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Wu, B., Zhou, M., Shen, X., Gao, Y., Silvera, R., & Yiu, G. (2013). Simple profile rectifications go a long way statistically exploring and alleviating the effects of sampling errors for program optimizations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7920 LNCS, pp. 654–678). Springer Verlag. https://doi.org/10.1007/978-3-642-39038-8_27
Mendeley helps you to discover research relevant for your work.