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包含各种预测器以提高性能。 未来的CPU可能会包含更多的预测器，特别是负载值预测器。 加载值预测器向需要加载结果的指令提供预测值，从而允许这些指令继续进行而无需等待加载的缓慢存储器访问完成。 因此，程序的执行时间减少。最近的工作负载值预测提出了复杂的混合预测与置信度估计。 虽然这些预测是相当有效的，其复杂性和大小的负面影响的性能参数，如关键路径长度，周期时间，芯片面积，功耗和散热。本研究的目标是减少尺寸的价值预测不降低性能，提高预测精度和覆盖率，并开发新的预测和置信度估计。 一个系统的搜索将找到新的预测，利用额外的价值局部性，并将确定更好的信心估计，减少昂贵的错误预测。 此外，将研究修复畸形预测和抑制错误预测的技术。 最后，将研究提高预测器利用率的方案和加快预测器访问的方法。 虽然所提出的想法在今天的系统中已经是有益的，但随着越来越多的CPU周期由于不断增长的负载延迟而被浪费，它们将变得更加重要。