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.