research-article

Seamless deductive inference via macros

Authors:

Arash Sahebolamri,

Kristopher MicinskiAuthors Info & Claims

CC 2022: Proceedings of the 31st ACM SIGPLAN International Conference on Compiler Construction

Pages 77 - 88

https://doi.org/10.1145/3497776.3517779

Published: 18 March 2022 Publication History

Abstract

We present an approach to integrating state-of-art bottom-up logic programming within the Rust ecosystem, demonstrating it with Ascent, an extension of Datalog that performs well against comparable systems. Rust’s powerful macro system permits Ascent to be compiled uniformly with the Rust code it’s embedded in and to interoperate with arbitrary user-defined components written in Rust, addressing a challenge in real-world use of logic programming languages: the fact that logical programs are parts of bigger software systems and need to interoperate with other components written in imperative programming languages.

We leverage Rust’s trait system to extend Datalog semantics with non-powerset lattices, much like Flix, and with user-defined data types much like Formulog and Souffle.

We use Ascent to re-implement the Rust borrow checker, a static analysis required by the Rust compiler. We evaluate our performance against Datafrog, Flix, and Soufflé using the borrow checker and other benchmarks, observing comparable performance to Datafrog and Soufflé, and speedups of around two orders of magnitude compared to Flix.

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Cited By

Klopp DErdweg SPacak A(2024)A Typed Multi-level Datalog IR and Its Compiler FrameworkProceedings of the ACM on Programming Languages10.1145/36897678:OOPSLA2(1586-1614)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689767
Bembenek AGreenberg MChong S(2024)Making Formulog Fast: An Argument for Unconventional Datalog EvaluationProceedings of the ACM on Programming Languages10.1145/36897548:OOPSLA2(1219-1248)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689754
Klopp DPacak AErdweg SChiba SThüm T(2024)Separate Compilation and Partial Linking: Modules for Datalog IRProceedings of the 23rd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences10.1145/3689484.3690737(94-106)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3689484.3690737
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Index Terms

Seamless deductive inference via macros
1. Software and its engineering
  1. Software organization and properties
    1. Software functional properties
      1. Formal methods
        Automated static analysis
2. Theory of computation
  1. Logic
    1. Constraint and logic programming
  2. Semantics and reasoning
    1. Program reasoning
      1. Program analysis
    2. Program semantics

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cover image ACM Conferences

CC 2022: Proceedings of the 31st ACM SIGPLAN International Conference on Compiler Construction

March 2022

253 pages

ISBN:9781450391832

DOI:10.1145/3497776

General Chairs:
Bernhard Egger
Seoul National University, South Korea
,
Aaron Smith
Microsoft, USA

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 18 March 2022

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CC '22: 31st ACM SIGPLAN International Conference on Compiler Construction

April 2 - 3, 2022

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Cited By

Klopp DErdweg SPacak A(2024)A Typed Multi-level Datalog IR and Its Compiler FrameworkProceedings of the ACM on Programming Languages10.1145/36897678:OOPSLA2(1586-1614)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689767
Bembenek AGreenberg MChong S(2024)Making Formulog Fast: An Argument for Unconventional Datalog EvaluationProceedings of the ACM on Programming Languages10.1145/36897548:OOPSLA2(1219-1248)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689754
Klopp DPacak AErdweg SChiba SThüm T(2024)Separate Compilation and Partial Linking: Modules for Datalog IRProceedings of the 23rd ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences10.1145/3689484.3690737(94-106)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3689484.3690737
Abeysinghe SXhebraj ARompf T(2024)Flan: An Expressive and Efficient Datalog Compiler for Program AnalysisProceedings of the ACM on Programming Languages10.1145/36329288:POPL(2577-2609)Online publication date: 5-Jan-2024
https://dl.acm.org/doi/10.1145/3632928
Herlihy AMartres GAilamaki AOdersky M(2024)Adaptive Recursive Query Optimization2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00035(368-381)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00035
Sahebolamri ABarrett LMoore SMicinski K(2023)Bring Your Own Data Structures to DatalogProceedings of the ACM on Programming Languages10.1145/36228407:OOPSLA2(1198-1223)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622840
Zhang YWang YFlatt OCao DZucker PRosenthal ETatlock ZWillsey M(2023)Better Together: Unifying Datalog and Equality SaturationProceedings of the ACM on Programming Languages10.1145/35912397:PLDI(468-492)Online publication date: 6-Jun-2023
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