The essence of LR parsing

Sperber, Michael; Thiemann, Peter

doi:10.1145/215465.215579

Cited by 15 publications

(9 citation statements)

References 7 publications

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“…Hence, no generation and recompilation is necessary to try out changes to a grammar. In the end, an automatic program transformation called partial evaluation produces a generated parsers from the general parser [16,18]. This guarantees consistency and ensures correctness.…”

Section: Methodsmentioning

confidence: 99%

Parameterized LR Parsing

Thiemann

Neubauer

2004

Electronic Notes in Theoretical Computer Science

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Common LR parser generators lack abstraction facilities for defining recurring patterns of productions. Although there are generators capable of supporting regular expressions on the right hand side of productions, no generator supports user defined patterns in grammars. Parameterized LR parsing extends standard LR parsing technology by admitting grammars with parameterized non-terminal symbols. A generator can implement such a grammar in two ways, either by expansion or directly. We develop the theory required for the direct implementation and show that it leads to significantly smaller parse tables and that it has fewer parsing conflicts than the expanded grammar. Attribute evaluation for a parameterized non-terminal is possible in the same way as before, if the semantic functions related to the non-terminal are polymorphic with respect to the parameter. We have implemented parameterized LR parsing in the context of Essence, a partial-evaluation based LR parser generator for Scheme.

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Section: Methodsmentioning

confidence: 99%

Parameterized LR Parsing

Thiemann

Neubauer

2004

Electronic Notes in Theoretical Computer Science

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“…We make use of the well known technique of multi-stage programming [35]. Sperber et al also use partial evaluation for optimizing LR parsers which are implemented as a functional-style library [31].…”

Section: Metaprogramming and Compiler Technologymentioning

confidence: 99%

Staged parser combinators for efficient data processing

Jonnalagedda

Coppey

Stucki

et al. 2014

Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages &Amp; Applications

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Parsers are ubiquitous in computing, and many applications depend on their performance for decoding data efficiently. Parser combinators are an intuitive tool for writing parsers: tight integration with the host language enables grammar specifications to be interleaved with processing of parse results. Unfortunately, parser combinators are typically slow due to the high overhead of the host language abstraction mechanisms that enable composition.We present a technique for eliminating such overhead. We use staging, a form of runtime code generation, to dissociate input parsing from parser composition, and eliminate intermediate data structures and computations associated with parser composition at staging time. A key challenge is to maintain support for input dependent grammars, which have no clear stage distinction.Our approach applies to top-down recursive-descent parsers as well as bottom-up nondeterministic parsers with key applications in dynamic programming on sequences, where we auto-generate code for parallel hardware. We achieve performance comparable to specialized, hand-written parsers.

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“…The staged parser combinator approach makes use of multi-stage programming [26] to evaluate overhead away. Sperber et al also use partial evaluation for optimising LR parsers which are implemented as a functional-style library [24].…”

Section: Parsingmentioning

confidence: 99%

Accelerating parser combinators with macros

Béguet

Jonnalagedda

2014

Proceedings of the Fifth Annual Scala Workshop

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Parser combinators provide an elegant way of writing parsers: parser implementations closely follow the structure of the underlying grammar, while accommodating interleaved host language code for data processing. However, the host language features used for composition introduce substantial overhead, which leads to poor performance.In this paper, we present a technique to systematically eliminate this overhead. We use Scala macros to analyse the grammar specification at compile-time and remove composition, leaving behind an efficient top-down, recursive-descent parser.We compare our macro-based approach to a staging-based approach using the LMS framework, and provide an experience report in which we discuss the advantages and drawbacks of both methods. Our library outperforms Scala's standard parser combinators on a set of benchmarks by an order of magnitude, and is 2x faster than code generated by LMS.

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The essence of LR parsing

Cited by 15 publications

References 7 publications

Parameterized LR Parsing

Parameterized LR Parsing

Staged parser combinators for efficient data processing

Accelerating parser combinators with macros

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