EAGER: Code-Improving Transformations for Branch Prediction

EAGER：分支预测的代码改进转换

• 批准号: 0952604
• 负责人: Daniel Jimenez
• 金额: $ 10万
• 依托单位: University of Texas at San Antonio
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0952604&HistoricalAwards=false
• 关键词: EAGER; Code; Improving; Transformations; Branch

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.

微处理器使用分支预测指标来预测程序的近期行为，以便在未来的指示上进行工作，从而减少了程序运行的时间。每个不正确的预测会浪费时间和能量。 因此，分支预测因子必须高度准确，并且准确性的较小提高可以为性能带来很大的好处。 分支预测的准确性可能会受到编译器在将高级程序转换为机器语言的编译器中的选择。 但是，当前的编译器没有考虑到翻译过程中其选择的许多方式可能会对分支机构的预测产生影响。 在该项目中，基于编译器技术改进的分支预测，开发了用于改善程序性能和减少能源消耗的技术。 具体而言，研究了代码放置对分支预测变量的影响，并开发了新的代码改造转换，以提高分支预测器的准确性。 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。 使用性能监控软件来测量新的代码改造转换的影响，以确定改进的幅度。 这项工作将导致在现代微处理器上运行的程序的性能和能源效率提高。