Using supervised learning techniques to improve compiler decisions

该建议侧重于开发三个框架，以改善优化编译器的开发过程和这些编译器产生的代码的性能。我们将开发一个机器学习框架，从一组方法特性中发现特定于方法的编译策略。第一个开发将针对IBM Testarossa编译器中的实时编译。我们还在开发一个数据挖掘框架，以便在分析数据时发现频繁的代码模式，从而帮助开发人员确定代码生成的潜在改进。第一个数据挖掘框架也将在IBM Testarossa编译器上工作。最后，我们将开发一个统计分析框架，该框架将能够从程序的几次执行中组合联合收割机配置文件，并为编译器提供有关应用程序行为变化的有意义的统计数据。这个框架的第一个实现将在IBM XL编译器中实现。

This proposal focuses on the development of three frameworks to improve both the development process of optimizing compilers and the performance of the code produced by such compilers. We will develop a machine-learning framework to discover method-specific compilation strategies from a set of method features. The first development will target just-in-time compilation in the IBM Testarossa Compiler. We are also developing a data-mining framework to enable the discovery of frequent code patterns in profiling data and thus to help developers to identify potential improvements to code generation. The first data-mining framework will also work on the IBM Testarossa Compiler. Finally, we will develop a framework for statistical profiling that will be able to combine profiles from several executions of a program and provide the compiler with meaningful statistics on the variability of the behavior of the application. The first implementation of this framework will be in the IBM XL compilers.