CAREER: Slice-Processing: Highly-Accurate Prediction for Future High-Performance Processors

职业：切片处理：对未来高性能处理器的高精度预测

• 批准号: 9984371
• 负责人: Andreas Moshovos
• 金额: $ 22.99万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9984371&HistoricalAwards=false
• 关键词: CAREER; Slice; Processing; Highly; Accurate

DATE: April 19, 2000PROPOSAL NUMBER: C-CR 9984371INSTITUTION: Northwestern UniversityPI: Andreas MoshovosTITLE: CAREER: Slice-Processing: Highly-Accurate Prediction for Future High-Performance ProcessorsABSTRACTPrediction-driven speculative techniques are used in virtually all modern, high-performance processors. Such techniques offer a promising direction for furtherboosting performance. However, the benefits so obtained are only as good as theunderlying prediction-based methods are at guessing what the program needs.This project investigates slice-prediction, a paradigm that aims at providinghighly accurate prediction beyond what is possible with the existing methods.Existing methods are primarily outcome-based since they rely on repeatingpatterns in a program's outcome stream (e.g., addresses or branch directions).However, such patterns do not always exist. More importantly, as currentoutcome-based methods are perfected, irregular patterns increasingly dominateperformance. Slice-prediction attacks such cases by predicting the computation-slice that produces such irregular yet performance-critical outcomes. Predictedslices are then treated as miniature, autonomous programs, which can precomputeperformance critical outcomes. The intuition is that while the outcome of aprogram can be irregular the method used is typically relatively simple andfairly stable.This project focuses on architecturally invisible, hardware implementations ofslice-prediction, and targets the following applications: (1) Branch Predictionand (2) Data Prefetching. Performance achieved by slice-prediction is evaluatedthrough a detailed software simulator over a set of widely used benchmarks.

日期：2000 年 4 月 19 日提案编号：C-CR 9984371 机构：西北大学 PI：Andreas Moshovos 标题：职业：切片处理：未来高性能处理器的高精度预测摘要预测驱动的推测技术几乎用于所有现代高性能处理器。这些技术为进一步提高性能提供了一个有希望的方向。然而，如此获得的好处仅与基于预测的底层方法猜测程序需要什么一样好。该项目研究切片预测，这是一种旨在提供超出现有方法可能的高度准确预测的范式。现有方法主要基于结果，因为它们依赖于程序输出流中的重复模式（例如，地址或分支方向）。但是，这些模式不 永远存在。更重要的是，随着当前基于结果的方法的完善，不规则模式越来越主导性能。切片预测通过预测产生此类不规则但性能关键结果的计算切片来攻击此类情况。然后，预测的切片被视为微型自主程序，可以预先计算性能关键结果。直觉是，虽然程序的结果可能不规则，但所使用的方法通常相对简单且相当稳定。该项目重点关注架构上不可见的切片预测的硬件实现，并针对以下应用：(1) 分支预测和 (2) 数据预取。切片预测实现的性能是通过详细的软件模拟器在一组广泛使用的基准上进行评估的。