Fast Parallel Equivalence Relations in a Datalog Compiler

Modern parallelizing Datalog compilers are employed in industrial applications such as networking and static program analysis. These applications regularly reason about equivalences, e.g., computing bitcoin user groups, fast points-to analyses, and optimal network routes. State-of-the-art Datalog en...

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Bibliographic Details
Published inProceedings / International Conference on Parallel Architectures and Compilation Techniques pp. 82 - 96
Main Authors Nappa, Patrick, Zhao, David, Subotic, Pavle, Scholz, Bernhard
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
Australia
C++ languages
Data structures
Datalog Compiler
Engines
Equivalence Relation
Parallel Data Structures
Program processors
Semantics
Semi-naïve Evaluation
Vegetation
Online AccessGet full text
ISSN2641-7936
DOI10.1109/PACT.2019.00015

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Summary:Modern parallelizing Datalog compilers are employed in industrial applications such as networking and static program analysis. These applications regularly reason about equivalences, e.g., computing bitcoin user groups, fast points-to analyses, and optimal network routes. State-of-the-art Datalog engines represent equivalence relations verbatim by enumerating all possible pairs in an equivalence class. This approach inhibits scalability for large datasets. In this paper, we introduce EQREL, a specialized parallel union-find data structure for scalable equivalence relations, and its integration into a Datalog compiler. Our data structure provides a quadratic worst-case speed-up and space improvement. We demonstrate the efficacy of our data structure in SOUFFLÉ, which is a Datalog compiler that synthesizes parallel C ++ code. We use real-world benchmarks and show that the new data structure scales on shared-memory multi-core architectures storing up to a half-billion pairs for a static program analysis scenario.
ISSN:2641-7936
DOI:10.1109/PACT.2019.00015