Glacial Variables: Towards Fully Automatic Run-Time Code Generation

Glacial Variables：迈向全自动运行时代码生成

• 批准号: 9610075
• 负责人: John Launchbury
• 金额: --
• 依托单位: Oregon Graduate Institute of Science & Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9610075&HistoricalAwards=false
• 关键词: Glacial; Variables; Towards; Fully; Automatic

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.

程序专业化有着悠久的历史。在自我修改代码的名义下，它在过去被用于性能改进。在部分求值的名称下，它已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，许多研究小组已经将运行时部分求值应用于运行时代码生成（RTCG），作为一种性能优化，但是它们需要大量的程序员注释来确定在何处应用转换。转换并不总是产生良好的优化，特别是在成本/收益权衡不是很明显的情况下。该项目正在开发自动方法（算法和启发式），用于选择在何处应用运行时代码生成（RTCG），并检查RTCG与标量和内存层次优化之间的交互。本项目将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已经应用于内核和语言解释器的基准测试，但还没有应用到更广泛的领域。这个项目旨在证明RTCG已经准备好用于生产，并且它可以部署在编译器中，而不需要对应用程序进行大量的重新编程来利用它的性能改进。