CAREER: Type-Directed Compilation

编译时类型信息被认为在标准ML等函数式语言的高效编译中非常有价值。但对于如何在真正的编译器中支持类型定向编译，以及从各种基于类型的优化中可以获得多大的性能提升，我们知之甚少。这项研究的目标是设计和识别能够显著提高ML程序性能的重要的类型定向编译技术。这些新的编译技术有望产生以下良好的结果：(1)ML程序中的数据对象(包括递归数据结构)将能够使用与C程序中一样高效的未装箱数据表示；(2)通过跨模块类型专门化和内联扩展，将有效地实现多态函数和参数化模块；(3)传播到编译器后端的类型信息将用于辅助程序分析，如闭包分析和指令调度。该职业奖的教育部分寻求两个目标：构建一个新的编译器实验室工具包(基于上述类型的ML编译器)，并将最先进的编译器技术纳入本科生和研究生课程。

Compile-time type information is believed to be very valuable in efficient compilation of functional languages such as Standard ML. But little is known on how to support type-directed compilation in real compilers and how much performance gain one can get from various type-based optimizations. The goal of this research is to design and identify important type-directed compilation techniques that can significantly improve the performance of ML programs. These new compilation techniques are expected to lead to the following nice results: (1) data objects (including recursive data structures) in ML programs will be able to use unboxed data representations that are as efficient as those in C programs; (2) polymorphic functions and parametrized modules will be implemented efficiently through cross-module type specialization and inlining expansion; (3) type information propagated into the compiler back end will be used to assist program analysis such as closure analysis and instruction scheduling. The educational component of this CAREER award seeks two objectives: the construction of a new compiler lab kit (based on the above type-based ML compiler) and the incorporation of state-of-the-art compiler technology into the undergraduate and graduate curriculum.