Efficient computation of LALR(1) look-ahead sets

DeRemer, Frank; Pennello, Thomas J.

doi:10.1145/872732.806968

Cited by 27 publications

(16 citation statements)

References 2 publications

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“…The former one scans the GMQL query and generates a list of tokens; the latter one identifies sub-sequences of the token list which correspond to grammar rules, using a LALR(1) algorithm [34]. When a statement is semantically valid, the compiler first infers the schema of the new introduced variable, then updates the internal state and finally emits the Apache Pig code that performs the requested operation.…”

Section: Gmql Compilermentioning

confidence: 99%

Data Management for Heterogeneous Genomic Datasets

Ceri

Kaitoua

Masseroli

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Next Generation Sequencing (NGS), a family of technologies for reading DNA and RNA, is changing biological research, and will soon change medical practice, by quickly providing sequencing data and high-level features of numerous individual genomes in different biological and clinical conditions. The availability of millions of whole genome sequences may soon become the biggest and most important "big data" problem of mankind. In this exciting framework, we recently proposed a new paradigm to raise the level of abstraction in NGS data management, by introducing a GenoMetric Query Language (GMQL) and demonstrating its usefulness through several biological query examples. Leveraging on that effort, here we motivate and formalize GMQL operations, especially focusing on the most characteristic and domain-specific ones. Furthermore, we address their efficient implementation and illustrate the architecture of the new software system that we have developed for their execution on big genomic data in a cloud computing environment, providing the evaluation of its performance. The new system implementation is available for download at the GMQL website (http://www.bioinformatics.deib.polimi.it/GMQL/); GMQL can also be tested through a set of predefined queries on ENCODE and Roadmap Epigenomics data at http://www.bioinformatics.deib.polimi.it/GMQL/queries/.

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Section: Gmql Compilermentioning

confidence: 99%

Data Management for Heterogeneous Genomic Datasets

Ceri

Kaitoua

Masseroli

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…Thus, Phase 0 computes LALR(1) parser tables. It does so in two steps: (1) compute LR(0) parser tables, and (2) compute the reduction lookahead sets using the technique described by [15]. As part of its algorithm, step 2 also computes a set of goto tables that the remaining IELR(1) phases require.…”

Section: Phase 0: Lalr(1)mentioning

confidence: 99%

Ielr(1)

Denny

Malloy

2008

Proceedings of the 2008 ACM Symposium on Applied Computing

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There has been a recent effort in the literature to reconsider grammar-dependent software development from an engineering point of view. As part of that effort, we examine a deficiency in the state of the art of practical LR parser table generation. Specifically, LALR sometimes generates parser tables that do not accept the full language that the grammar developer expects, but canonical LR is too inefficient to be practical. In response, many researchers have attempted to develop minimal LR parser table generation algorithms. In this paper, we demonstrate that a well known algorithm described by David Pager and implemented in Menhir, the most robust minimal LR(1) implementation we have discovered, does not always achieve the full power of canonical LR(1) when the given grammar is non-LR(1) coupled with a specification for resolving conflicts. We also outline an original minimal LR(1) algorithm, IELR(1), which we have implemented as an extension of GNU Bison and which does not exhibit this deficiency. Finally, using our implementation, we demonstrate the relevance of this deficiency for several real-world grammars, and we show that our implementation is feasible for generating minimal LR(1) parsers for those grammars.

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“…Only some practical improvements in LALR(k) parser construction not decreasing the exponential worstcase nature of the algorithm have been reported (for example [3,7]). Even finding a nondeterministic polynomial time algorithm for LALR(k) testing seems impossible.…”

Section: Introductionmentioning

confidence: 99%

LALR(k) testing is PSPACE-complete

Ukkonen¹,

Soisalon-Soininen²

1981

Proceedings of the Thirteenth Annual ACM Symposium on Theory of Computing - STOC '81

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The problem of testing whether or not an arbitrary context-free grammar is LALR(k) for a fixed integer k ~ I (i.e. only the subject grammar is a problem parameter) is shown to be PSPACE-complete. The result is in contrast with testing the membership in several other easily parsed classes of grammars, such as LR(k), SLR(k), LC(k) and LL(k) grammars, for which deterministic polynomial time membership tests are known. The PSPACE-hardness of the problem is proved using a transformation from the finite state automaton non-universality problem.A nondeterministic algorithm for constructing sets of LR(k) items leads to a polynomially space bounded algorithm for LALR(k) testing.

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Efficient computation of LALR(1) look-ahead sets

Cited by 27 publications

References 2 publications

Data Management for Heterogeneous Genomic Datasets

Data Management for Heterogeneous Genomic Datasets

Ielr(1)

LALR(k) testing is PSPACE-complete

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