Recursive descent parser

In computer science, a recursive descent parser is a kind of top-down parser built from a set of mutually recursive procedures (or a non-recursive equivalent) where each such procedure implements one of the nonterminals of the grammar. Thus the structure of the resulting program closely mirrors that of the grammar it recognizes. A predictive parser is a recursive descent parser that does not require backtracking. Predictive parsing is possible only for the class of LL(k) grammars, which are the context-free grammars for which there exists some positive integer k that allows a recursive descent parser to decide which production to use by examining only the next k tokens of input. The LL(k) grammars therefore exclude all ambiguous grammars, as well as all grammars that contain left recursion. Any context-free grammar can be transformed into an equivalent grammar that has no left recursion, but removal of left recursion does not always yield an LL(k) grammar. A predictive parser runs

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En informatique, un compilateur de compilateur est un programme capable de produire la totalité ou certaines parties du code source d'un compilateur (partie analyse lexicale, partie analyse syntaxiqu

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CS-452: Foundations of software

The course introduces the foundations on which programs and programming languages are built. It introduces syntax, types and semantics as building blocks that together define the properties of a program part or a language. Students will learn how to apply these concepts in their reasoning.

CS-320: Computer language processing

We teach the fundamental aspects of analyzing and interpreting computer languages, including the techniques to build compilers. You will build a working compiler from an elegant functional language into the new web standard for portable binaries called WebAssembly ( https://webassembly.org/ )

CS-431: Introduction to natural language processing

The objective of this course is to present the main models, formalisms and algorithms necessary for the development of applications in the field of natural language information processing. The concepts introduced during the lectures will be applied during practical sessions.

Accelerating parser combinators with macros

Eric Beguet, Manohar Jonnalagedda

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.

ACM Press2014

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Staged parser combinators for efficient data processing

Thierry Coppey, Manohar Jonnalagedda, Martin Odersky, Tiark Rompf, Sandro Stucki

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.

ACM Press2014

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Staged Parser Combinators for Efficient Data Processing

Thierry Coppey, Manohar Jonnalagedda, Martin Odersky, Tiark Rompf, Sandro Stucki

Assoc Computing Machinery2014

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