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Call Sequence Prediction through Probabilistic Calling Automata
Zhao, Zhijia Zhou, Mingzhou Sun, Jianhua Wu, Bo Ding, Yufei Shen, Xipeng
Zhao, Zhijia
Zhou, Mingzhou
Sun, Jianhua
Wu, Bo
Ding, Yufei
Shen, Xipeng
Abstract
Predicting a sequence of upcoming function calls is important for optimizing programs written in modern managed languages (e.g., Java, Javascript, C#.) Existing function call predictions are mainly built on statistical patterns, suitable for predicting a single call but not a sequence of calls. This paper presents a new way to enable call sequence prediction, which exploits program structures through Probabilistic Calling Automata (PCA), a new program representation that captures both the inherent ensuing relations among function calls, and the probabilistic nature of execution paths. It shows that PCA-based prediction outperforms existing predictions, yielding substantial speedup when being applied to guide Just-In-Time compilation. By enabling accurate, efficient call sequence prediction for the first time, PCA-based predictors open up many new opportunities for dynamic program optimizations.
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2014-01-01
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Computer Science
DOI
https://doi.org/10.1145/2660193.2660221