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Cited by 324

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

The execution algorithm of mercury, an efficient purely declarative logic programming language

Open access
Somogyi, Zoltan, Henderson, Fergus, Conway, Thomas·1996·The Journal of Logic Programming

Abstract

We introduce Mercury, a new purely declarative logic programming language designed to provide the support that groups of application programmers need when building large programs. Mercury's strong type, mode, and determinism systems improve program reliability by catching many errors at compile time. We present a new and relatively simple execution model that takes advantage of the information these systems provide, yielding very efficient code. The Mercury compiler uses this execution model to generate portable C code. Our benchmarking shows that the code generated by our implementation is significantly faster than the code generated by mature optimizing implementations of other logic programming languages.

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Crossref · 10.1016/s0743-1066(96)00068-4

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Metadata

Authors
Somogyi, Z., Henderson, F., & Conway, T.
Venue
The Journal of Logic Programming
Publisher
Elsevier BV
Published
1996-10
Volume
29
Issue
1-3
Pages
17-64
ISSN
0743-1066 (ISSN-L)
OpenAlex
W1982243747
Cited by
324
References
11
FWCI
17.78
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: The execution algorithm of mercury, an efficient purely declarative logic programming language. https://app.cochat.ai/papers/10.1016/s0743-1066(96)00068-4
The paper
Somogyi, Z., Henderson, F., & Conway, T. (1996). The execution algorithm of mercury, an efficient purely declarative logic programming language. The Journal of Logic Programming. https://doi.org/10.1016/s0743-1066(96)00068-4

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.