On the complexity and performance of parsing with derivatives

Author:

Adams Michael D.1,Hollenbeck Celeste1,Might Matthew1

Affiliation:

1. University of Utah, USA

Abstract

Current algorithms for context-free parsing inflict a trade-off between ease of understanding, ease of implementation, theoretical complexity, and practical performance. No algorithm achieves all of these properties simultaneously. Might et al. introduced parsing with derivatives, which handles arbitrary context-free grammars while being both easy to understand and simple to implement. Despite much initial enthusiasm and a multitude of independent implementations, its worst-case complexity has never been proven to be better than exponential. In fact, high-level arguments claiming it is fundamentally exponential have been advanced and even accepted as part of the folklore. Performance ended up being sluggish in practice, and this sluggishness was taken as informal evidence of exponentiality. In this paper, we reexamine the performance of parsing with derivatives. We have discovered that it is not exponential but, in fact, cubic. Moreover, simple (though perhaps not obvious) modifications to the implementation by Might et al. lead to an implementation that is not only easy to understand but also highly performant in practice.

Funder

Defense Advanced Research Projects Agency

National Science Foundation

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Graphics and Computer-Aided Design,Software

Reference10 articles.

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3. URL https://github. com/Engelberg/instaparse. Abraham Flaxman, Aram W. Harrow, and Gregory B. Sorkin. Strings with maximally many distinct subsequences and substrings;Engelberg Mark;The Electronic Journal of Combanatorics,2015

4. A unified approach to global program optimization

5. A Completeness Theorem for Kleene Algebras and the Algebra of Regular Events

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1. Staged selective parser combinators;Proceedings of the ACM on Programming Languages;2020-08-02

2. Derivatives of Parsing Expression Grammars;Electronic Proceedings in Theoretical Computer Science;2017-08-21

3. Partial Derivatives for Context-Free Languages;Lecture Notes in Computer Science;2017

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