Next-Generation Load-Value Predictors

下一代负载值预测器

• 批准号: 0208567
• 负责人: Martin Burtscher
• 金额: --
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0208567&HistoricalAwards=false
• 关键词: Next; Generation; Load; Value; Predictors

Current high-end microprocessors incorporate a variety of predictors to improve performance. Future CPUs will likely include even more predictors, in particular load-value predictors. Load-value predictors provide predicted values to instructions that need the result of a load, thereby allowing these instructions to proceed without waiting for the load's slow memory access to complete. Thus, the program execution time is reduced.Recent work on load-value prediction proposes sophisticated hybrid predictors with confidence estimators. While these predictors are quite effective, their complexity and size negatively affect performance parameters such as critical-path length, cycle time, chip area, power consumption, and heat dissipation.The goal of this research is to reduce the size of value predictors without decreasing performance, to improve the prediction accuracy and coverage, and to develop new predictors and confidence estimators. A systematic search will find novel predictors that exploit additional value locality and will identify better confidence estimators that reduce costly mispredictions. Moreover, techniques that repair malformed predictions and inhibit wrong predictions will be investigated. Finally, schemes to enhance the predictor utilization and approaches to speed up predictor accesses will be researched. While the proposed ideas are already beneficial in today's systems, they will become even more important as increasing numbers of CPU cycles are wasted due to growing load latencies.

当前的高端微处理器结合了各种预测因子，以提高性能。 未来的CPU可能会包括更多的预测指标，特别是负载值预测指标。 负载值预测指标为需要负载结果的指令提供了预测的值，从而允许这些说明继续执行，而无需等待负载的缓慢内存访问以完成。 因此，减少了程序的执行时间。负载价值预测上的工作提出了具有置信度估计器的复杂混合预测指标。 尽管这些预测因素非常有效，但它们的复杂性和大小会对性能参数产生负面影响，例如临界路径长度，周期时间，芯片面积，功耗和散热。这项研究的目的是减少价值预测变量的大小，而不会降低性能，以提高预测准确性和覆盖率，并开发新的预测指标和置信度估算值。 系统的搜索将找到利用额外价值区域的新型预测指标，并确定更好的置信估计器，以减少昂贵的错误预测。 此外，将研究修复预测和抑制错误预测的技术。 最后，将研究为增强预测器利用和加快预测访问的方法的方案。 尽管拟议的想法在当今的系统中已经有益，但由于负载潜伏期的增长，浪费了越来越多的CPU周期，它们将变得更加重要。