EAGER: Code-Improving Transformations for Branch Prediction

微处理器使用分支预测器来预测程序的近期行为，以便可以提早开始处理未来的指令，从而减少程序运行所需的时间。每一个错误的预测都是浪费时间和精力。因此，分支预测器必须具有很高的精确度，在精确度上稍有提高就能为性能带来很大好处。在将高级程序翻译成机器语言的编译器中所做的选择会显著影响分支预测器的准确性。然而，当前的编译器没有考虑到他们在翻译期间的选择可能对分支预测产生影响的许多方式。在这个项目中，通过编译器技术，开发了基于改进的分支预测来提高程序性能和降低能源消耗的技术。具体地说，研究了代码放置对分支预测器的影响，并开发了新的代码改进转换来提高分支预测器的精度。这些研究是在包括Intel和AMD微处理器在内的真实系统上进行的。使用性能监控软件测量新的代码改进转换的影响，以确定改进的幅度。这项工作将提高在现代微处理器上运行的程序的性能和能效。

Microprocessors use branch predictors to predict the near-term behavior of a program so that work on future instructions may begin early, reducing the amount of time a program takes to run. Each incorrect prediction wastes time and energy. Thus, branch predictors must be highly accurate, and a small improvement in accuracy can give a large benefit for performance. Branch predictor accuracy can be significantly affected by choices made in the compiler that translates high-level programs to machine-language. However, current compilers do not take into account many of the ways in which their choices during translation may have an impact on branch prediction. In this project, technologies are developed for improving program performance and reducing energy consumption based on improved branch prediction through compiler techniques. Specifically, the impact of code placement on branch predictors is studied and new code-improving transformations are developed that improve branch predictor accuracy. The studies are carried out on real systems including Intel and AMD microprocessors. The impact of the new code-improving transformations is measured using performance monitoring software to determine the magnitude of the improvement. This work will result in improved performance and energy-efficiency for programs running on modern microprocessors.