一句话:提出有限多态演算与类型宇宙重构,消除多态编译中的类型传递抽象并加速代码。
针对多态语言中类型导向编译常需传递类型信息、带来运行时开销的问题,本文提出一种有限多态的类型论方法。作者定义带有限类型宇宙的谓词式二阶 lambda 演算,将类型抽象约束到有限实例类型集合,并相对类型传递操作语义证明类型可靠性。为将标准二阶 lambda 演算项转化为该演算,提出宇宙重构算法,可判定并构造对应项。基于此给出多态函数的类型优化方法,在 SML# 编译器中针对原生无标签数据表示和记录多态实现。基准测试表明,83.79% 的类型传递抽象被消除,编译代码平均加速 15.28%。
We develop a type-theoretical method for optimizing type directed compilation of polymorphic languages, implement the method in SML#, which is a full-scale compiler of Standard ML extended with several advanced features that require type-passing operational semantics, and report its effectiveness through performance evaluation. For this purpose, we first define a predicative second-order lambda calculus with finitary polymorphism, where each type abstraction is explicitly constrained to a finite type universe, and establishes the type soundness with respect to a type-passing operational semantics. Different from a calculus with stratified type universes, type universes of the calculus are terms that represent a finite set of instance types. We then develop a universe reconstruction algorithm that takes a term of the standard second-order lambda calculus, checks if the term is typable with finitary polymorphism, and, if typable, constructs a term in the calculus of finitary polymorphism. Based on these results, we present a type-based optimization method for polymorphic functions. Since our formalism is based on the second-order lambda calculus, it can be used to optimize various polymorphic languages. We implement the optimization method for native (tag-free) data representation and record polymorphism, and evaluate its effectiveness through benchmarks. The evaluation shows that 83.79% of type passing abstractions are eliminated, and achieves the average of 15.28% speed-up of compiled code.
@article{paperbot268,
title = {Finitary polymorphism for optimizing type-directed compilation},
author = {Atsushi Ohori and Katsuhiro Ueno and Hisayuki Mima},
journal = {Proceedings of the ACM on Programming Languages},
volume = {2},
number = {ICFP},
year = {2018},
doi = {10.1145/3236776}
}