Adaptive Concretization for Parallel Program Synthesis
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Solar-Lezama_Adaptive concretization.pdf
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384.49 KB
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Author(s)
Jeon, Jinseong
Foster, Jeffrey S.
Qiu, Xiaokang
Solar Lezama, Armando
Date Issued
August 28, 2017
Journal
Computer Aided Verification
Publisher
Springer-Verlag
Citation
Jeon, Jinseong, et al. “Adaptive Concretization for Parallel Program Synthesis.” Lecture Notes in Computer Science (2015): 377–394. © 2015 Springer International Publishing Switzerland
Version
Author's final manuscript
Abstract
Program synthesis tools work by searching for an implementation that satisfies a given specification. Two popular search strategies are symbolic search, which reduces synthesis to a formula passed to a SAT solver, and explicit search, which uses brute force or random search to find a solution. In this paper, we propose adaptive concretization, a novel synthesis algorithm that combines the best of symbolic and explicit search. Our algorithm works by partially concretizing a randomly chosen, but likely highly influential, subset of the unknowns to be synthesized. Adaptive concretization uses an online search process to find the optimal size of the concretized subset using a combination of exponential hill climbing and binary search, employing a statistical test to determine when one degree of concretization is sufficiently better than another. Moreover, our algorithm lends itself to a highly parallel implementation, further speeding up search. We implemented adaptive concretization for Sketch and evaluated it on a range of benchmarks. We found adaptive concretization is very effective, outperforming Sketch in many cases, sometimes significantly, and has good parallel scalability.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-21668-3_22
