Profile-guided元编程

Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2015-06-03 DOI:10.1145/2737924.2737990

W. J. Bowman, Swaha Miller, Vincent St-Amour, R. Dybvig

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引用次数: 8

摘要

当代的编译器系统，如GCC、。net和LLVM，在低级的中间代码和基本块上结合了配置文件引导的优化(pgo)，与纯静态启发式相比，效果令人印象深刻。最近的工作表明，概要信息对于通过元编程执行源到源的优化也很有用。例如，使用分析信息来为有关数据结构和算法的决策提供信息，可能会导致性能的渐进改进。我们提出了一个通用元编程系统中配置文件引导元编程的设计。我们的设计在特定的分析器和元编程系统上是参数化的。我们在两个不同的元编程系统中实现了这种设计——Chez Scheme和Racket的语法扩展系统——并提供了几个概要文件引导的元程序作为可用性案例研究。

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Profile-guided meta-programming

Contemporary compiler systems such as GCC, .NET, and LLVM incorporate profile-guided optimizations (PGOs) on low-level intermediate code and basic blocks, with impressive results over purely static heuristics. Recent work shows that profile information is also useful for performing source-to-source optimizations via meta-programming. For example, using profiling information to inform decisions about data structures and algorithms can potentially lead to asymptotic improvements in performance. We present a design for profile-guided meta-programming in a general-purpose meta-programming system. Our design is parametric over the particular profiler and meta-programming system. We implement this design in two different meta-programming systems---the syntactic extensions systems of Chez Scheme and Racket---and provide several profile-guided meta-programs as usability case studies.

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Proceedings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation

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