R可以融化大脑:用于一流环境的IR和懒惰的有效论证

O. Flückiger, Guido Chari, Jan Jecmen, Ming-Ho Yee, Jakob Hain, J. Vitek
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引用次数: 14

摘要

R编程语言结合了许多被认为很难分析和有效实现的特性:动态类型、反射、惰性求值、向量化基本类型、一等闭包和大量使用本机代码。此外,变量作用域在运行时被具体化为一等环境。这些特性的组合使大多数静态程序分析技术变得不切实际,因此,基于它们的编译器优化是无效的。我们介绍了我们在PIR方面的工作,PIR是一种中间表示,明确支持第一级环境和有效的惰性求值。我们描述了关于PIR的两种数据流分析:第一个允许对变量及其环境进行推理,第二个可以推断在何处对参数进行评估。利用它们的结果,我们将展示如何省略环境创建和内联函数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
R melts brains: an IR for first-class environments and lazy effectful arguments
The R programming language combines a number of features considered hard to analyze and implement efficiently: dynamic typing, reflection, lazy evaluation, vectorized primitive types, first-class closures, and extensive use of native code. Additionally, variable scopes are reified at runtime as first-class environments. The combination of these features renders most static program analysis techniques impractical, and thus, compiler optimizations based on them ineffective. We present our work on PIR, an intermediate representation with explicit support for first-class environments and effectful lazy evaluation. We describe two dataflow analyses on PIR: the first enables reasoning about variables and their environments, and the second infers where arguments are evaluated. Leveraging their results, we show how to elide environment creation and inline functions.
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