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P2 Lambda calculus with algebraic simplification for reduction parallelisation: Extended study

面向归约并行化的代数化简 Lambda 演算:扩展研究
JFP vol.312021匹配度 2/10 · 该论文属于纯程序语言理论与类型系统方向,研究 λ 演算与并行归约的代数化简,并未涉及用户核心关注的 PL↔AI、Agentic 程序、静态分析、浮点程序分析或贝叶斯程序分析。虽然与 PL 理论相关,但距离研究画像的核心问题较远。引用 1 inbox
AKIMASA MORIHATA
crossref: 2026-09-10

AI 速览

一句话:提出带代数化简的简单类型 λ 演算 λ_AS,用 δ 抽象和类型系统为复杂归约的并行化提供等式推理基础。

并行归约是并行编程的核心,但哪些非平凡汇总可表示为并行归约仍缺乏理解。本文提出 λ_AS:一个带代数化简的简单类型 Lambda 演算,为通过等式推理研究复杂归约的并行化提供基础。其核心是 δ 抽象。δ 抽象与标准 λ 抽象观察等价,但会利用结合律、交换律等代数性质,在参数到达前先化简函数体;类型系统则保证这些化简不会引入严重开销。作者通过多个复杂并行归约的推导实例展示其有效性,包括含多个归约算子的情形、带条件跳转的循环、前缀和模式以及树操作。

问题
复杂归约能否以及如何并行化缺乏统一的形式化理解,尤其是非平凡汇总如何表示为并行归约。
方法
提出简单类型 Lambda 演算 λ_AS,引入 δ 抽象,使函数体在参数到达前利用结合律、交换律等代数性质化简,并用类型系统控制化简开销;通过等式推理推导并行归约。
评估
通过在多个复杂并行归约实例上的推导与展示来评估,包括多归约算子、带条件跳转的循环、前缀和模式和树操作。
原文摘要(Abstract)

Abstract Parallel reduction is a major component of parallel programming and widely used for summarisation and aggregation. It is not well understood, however, what sorts of non-trivial summarisations can be implemented as parallel reductions. This paper develops a calculus named λ AS , a simply typed lambda calculus with algebraic simplification. This calculus provides a foundation for studying a parallelisation of complex reductions by equational reasoning. Its key feature is δ abstraction. A δ abstraction is observationally equivalent to the standard λ abstraction, but its body is simplified before the arrival of its arguments using algebraic properties such as associativity and commutativity. In addition, the type system of λ AS guarantees that simplifications due to δ abstractions do not lead to serious overheads. The usefulness of λ AS is demonstrated on examples of developing complex parallel reductions, including those containing more than one reduction operator, loops with conditional jumps, prefix sum patterns and even tree manipulations.

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BibTeX
@article{paperbot1234,
  title = {Lambda calculus with algebraic simplification for reduction parallelisation: Extended study},
  author = {AKIMASA MORIHATA},
  journal = {Journal of Functional Programming},
  volume = {31},
  year = {2021},
  doi = {10.1017/s0956796821000058}
}