%0 Conference Paper %1 Zhao:14:CSP %A Zhao, Zhijia %A Wu, Bo %A Zhou, Mingzhou %A Ding, Yufei %A Sun, Jianhua %A Shen, Xipeng %A Wu, Youfeng %B Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications %D 2014 %I Association for Computing Machinery %K CallGraph automata call calling compilation dynamic function just-in-time optimizations parallel prediction probabilistic sequence %P 745--762 %R 10.1145/2660193.2660221 %T Call Sequence Prediction through Probabilistic Calling Automata %X 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. %@ 9781450325851

@inproceedings{Zhao:14:CSP, 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.}, added-at = {2021-03-21T15:24:53.000+0100}, author = {Zhao, Zhijia and Wu, Bo and Zhou, Mingzhou and Ding, Yufei and Sun, Jianhua and Shen, Xipeng and Wu, Youfeng}, biburl = {https://www.bibsonomy.org/bibtex/20f152f401033cf4f5cf4405d634b408b/gron}, booktitle = {Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications}, doi = {10.1145/2660193.2660221}, interhash = {f4a065c3153c42392ecd4541d902336c}, intrahash = {0f152f401033cf4f5cf4405d634b408b}, isbn = {9781450325851}, keywords = {CallGraph automata call calling compilation dynamic function just-in-time optimizations parallel prediction probabilistic sequence}, location = {Portland, Oregon, USA}, numpages = {18}, pages = {745--762}, publisher = {Association for Computing Machinery}, series = {OOPSLA '14}, timestamp = {2021-03-21T15:24:53.000+0100}, title = {{Call Sequence Prediction through Probabilistic Calling Automata}}, year = 2014 }