Fast Parallel Equivalence Relations in a Datalog Compiler

• Published in: Proceedings / International Conference on Parallel Architectures and Compilation Techniques pp. 82 - 96
• Main Authors: Nappa, Patrick, Zhao, David, Subotic, Pavle, Scholz, Bernhard
• Format: Conference Proceeding
• Language: English
• 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
• ISSN: 2641-7936
• DOI: 10.1109/PACT.2019.00015

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.