Optimizing SGLR Parser Performance

Bachelor Thesis (2021)
Author(s)

J. de Ruiter (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Eelco Visser – Mentor (TU Delft - Programming Languages)

Jasper Denkers – Mentor (TU Delft - Programming Languages)

D.A.A. Pelsmaeker – Mentor (TU Delft - Programming Languages)

RangaRao Venkatesha Prasad – Graduation committee member (TU Delft - Embedded Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2021 Justin de Ruiter
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Justin de Ruiter
Graduation Date
02-07-2021
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

The Scannerless Generalized-LR (SGLR) parsing algorithm allows parsing of declarative and modular syntax definitions. However, SGLR is notorious for having low performance, negatively impacting its adoption in practice. This paper presents several performance optimizations for JSGLR2, which is an implementation of SGLR. All optimizations are implemented and evaluated in parallel, which is possible due to JSGLR2's modular architecture. The evaluation is performed using existing sources from three different languages. A combined speed-up of 9% up to 44% is achieved, improving the practicality of JSGLR2.

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