通过去专门化关系实现对Datalog的高效解释器

Xiaowen Hu, David Zhao, Herbert Jordan, Bernhard Scholz
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引用次数: 1

摘要

Datalog作为各种用例的标准工具正变得越来越流行。现代Datalog引擎可以通过为关系操作专门化数据结构来实现高性能。例如,Datalog引擎souffl通过一个专门用于关系的数据结构的合成器实现了高性能。然而,合成器不能总是被部署,并且需要一个快速的解释器。本文介绍了souffl树解释器(STI)的设计与实现。STI性能的关键是支持对关系的快速操作。我们通过去专门化数据结构来获得快速的操作,这样它们就可以在虚拟执行环境中工作。与合成代码相比,我们的新解释器的性能降低了1.32到5.67倍。如果还考虑合成器的编译时间开销,解释器在第一次运行时平均可以快6.46倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient interpreter for Datalog by de-specializing relations
Datalog is becoming increasingly popular as a standard tool for a variety of use cases. Modern Datalog engines can achieve high performance by specializing data structures for relational operations. For example, the Datalog engine Soufflé achieves high performance with a synthesizer that specializes data structures for relations. However, the synthesizer cannot always be deployed, and a fast interpreter is required. This work introduces the design and implementation of the Soufflé Tree Interpreter (STI). Key for the performance of the STI is the support for fast operations on relations. We obtain fast operations by de-specializing data structures so that they can work in a virtual execution environment. Our new interpreter achieves a competitive performance slowdown between 1.32 and 5.67× when compared to synthesized code. If compile time overheads of the synthesizer are also considered, the interpreter can be 6.46× faster on average for the first run.
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