CAREER: Branch Prediction

职业：分支预测

• 批准号: 1332597
• 负责人: Daniel Jimenez
• 金额: $ 1.93万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1332597&HistoricalAwards=false
• 关键词: CAREER; Branch; Prediction

Microprocessors use devices called 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 the program takes to run. Branch predictors must be highly accurate, and a small improvement in accuracy can give a large benefit for performance. This project is a principled approach to continuing the study of branch prediction. Several new ways to understand and improve branch prediction will be explored:1) Exploring the limits of the potential of branch prediction to improve performance by developing a model of an idealistic branch predictor given reasonable assumptions;2) Improving technologies for running computer programs on real computer systems so that these programs will have better branch prediction accuracy;3) Discovering ways of improving the communication between computer programs and computer systems such that information available to a computer program can be used to improve the accuracy of branch prediction in a computer system; and4) Working on new branch predictor designs for future computer systems, incorporating techniques from other disciplines such as machine learning, i.e., the study of how computer systems can learn by observing data.In each of these areas, technological constraints on branch prediction will be taken into account. In particular, a branch predictor must act very quickly to deliver its prediction in time to improve performance, and it should do so in an energy-efficient way. This research will be brought to the classroom with a special seminar class on the interaction of research into computer systems and research on machine learning.

微处理器使用称为分支预测变量的设备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。 分支预测变量必须高度准确，并且准确性的较小提高可以为性能带来很大的好处。 该项目是继续进行分支预测研究的原则方法。 将探讨几种理解和改善分支预测的新方法：1）通过开发一个理想主义分支预测的模型来探索分支预测的潜在提高性能的局限性，给定合理的假设； 2）改进在实际计算机系统上运行计算机程序的技术，以便这些程序在分支机构上具有更好的分支程序的准确性； 3）在计算机系统之间进行计算机的准确性; 3）在计算机系统之间进行计算机的沟通方式； 3）在计算机系统之间进行计算机的通信方法； 系统;和4）为未来的计算机系统进行新的分支预测设计，并结合了其他学科（例如机器学习）的技术，即，研究计算机系统如何通过观察数据进行学习。在这些领域中的每个领域，将考虑有关分支预测的技术限制。 特别是，分支预测变量必须非常迅速地采取行动以进行时间的预测以提高性能，并且应该以节能的方式进行。 这项研究将通过有关计算机系统研究与机器学习研究的研究相互作用的特殊研讨会将其带到教室。