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Staged parser combinators for efficient data processing

Authors: Manohar Jonnalagedda; Thierry Coppey; Sandro Stucki; Tiark Rompf; Martin Odersky;

Staged parser combinators for efficient data processing

Abstract

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 non-deterministic 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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    17
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
17
Top 10%
Top 10%
Top 10%
bronze