Glacial Variables: Towards Fully Automatic Run-Time Code Generation

课程专业化有一段辉煌的历史。在自我修改代码的名义下，它过去曾被用来提高性能。在部分求值的名义下，它被用作从声明性语言的解释器自动生成编译器的转换。最近，许多研究小组已经应用运行时部分求值来执行运行时代码生成(RTCG)作为性能优化，但他们需要大量的程序员注释来确定在哪里应用转换。转换并不总是产生良好的优化，特别是在成本/收益权衡不是非常明显的情况下。该项目正在开发自动方法(算法和启发式)，用于选择在哪里应用运行时代码生成(RTCG)，并检查RTCG与标量和内存层次结构优化之间的交互。该项目正在将RTCG应用于大量完整的科学应用，以确定其适用性。到目前为止，RTCG还被应用于基准内核和语言解释器，但还没有应用到任何更广泛的领域。该项目旨在证明RTCG已准备好投入生产使用，并且它可以部署在编译器中，而不需要对应用程序进行广泛的重新编程来利用其性能改进。

Program specialization has a distinguished history. Under the name of self-modifying code it has been used in the past for performance improvements. Under the name of partial evaluation it has been used as a transformation to automatically generate compilers from interpreters for declarative languages. Recently, a number of research groups have applied run-time partial evaluation to do run-time code generation (RTCG) as a performance optimization, but they require extensive programmer annotations to determine where to apply the transformation. Transformations do not always yield good optimizations, particularly where the cost/benefit tradeoffs are not highly obvious. This project is developing automatic methods (algorithms and heuristics) for selecting where to apply run-time code generation (RTCG), and examining the interaction between RTCG and scalar and memory-hierarchy optimizations. The project is applying RTCG to a large number of complete scientific applications to determine its applicability. Heretofore RTCG has been applied to benchmark kernels and language interpreters, but not to any wider domains. This project aims to demonstrate that RTCG is ready for production use and that it can be deployed in compilers without requiring extensive reprogramming of applications to make use of its performance improvements.