@inproceedings{b9f5574c46a641fd9414e33c28489b1c,
title = "White-Box Program Tuning",
abstract = "Many programs or algorithms are largely parameterized, especially those based on heuristics. The quality of the results depends on the parameter setting. Different inputs often have different optimal settings. Program tuning is hence of great importance. Existing tuning techniques treat the program as a black-box and hence cannot leverage the internal program states to achieve better tuning. We propose a white-box tuning technique that is implemented as a library. The user can compose complex program tuning tasks by adding a small number of library calls to the original program and providing a few callback functions. Our experiments on 13 widely-used real-world programs show that our technique substantially improves data processing results and outperforms OpenTuner, the state-of-the-art black-box tuning technique.",
keywords = "Black-box tuning, Parameter tuning, Parameterized program, White-box tuning",
author = "Lee, {Wen Chuan} and Yingqi Liu and Peng Liu and Shiqing Ma and Hongjun Choi and Xiangyu Zhang and Rajiv Gupta",
note = "Funding Information: We thank the anonymous reviewers for their constructive comments. This research was supported, in part, by DARPA under contract FA8650-15-C-7562, NSF under awards 1748764 and 1409668, ONR under contracts N000141410468 and N000141712947, and Sandia National Lab under award 1701331. Any opinions, findings, and conclusions in this paper are those of the authors only and do not necessarily reflect the views of our sponsors. Funding Information: This research was supported, in part, by DARPA under contract FA8650-15-C-7562, NSF under awards 1748764 and 1409668, ONR under contracts N000141410468 and N000141712947, and Sandia National Lab under award 1701331 Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/ACM International Symposium on Code Generation and Optimization, CGO 2019 ; Conference date: 16-02-2019 Through 20-02-2019",
year = "2019",
month = mar,
day = "5",
doi = "10.1109/CGO.2019.8661177",
language = "English (US)",
series = "CGO 2019 - Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "122--135",
editor = "Tipp Moseley and Alexandra Jimborean and Kandemir, {Mahmut Taylan}",
booktitle = "CGO 2019 - Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization",
address = "United States",
}