CAREER: Branch Prediction

微处理器使用称为分支预测器的设备来预测程序的近期行为，以便将来的指令工作可以提前开始，减少程序运行所需的时间。分支预测器必须是高度准确的，准确度的一个小改进可以给性能带来很大的好处。该项目是继续研究分支预测的原则性方法。本文将探索几种理解和改进分支预测的新方法：1)在合理假设的情况下，通过开发理想分支预测器模型，探索分支预测潜力的局限性，以提高性能；2)改进计算机程序在真实计算机系统上运行的技术，使程序具有更好的分支预测精度；(3)发现改进计算机程序与计算机系统之间通信的方法，使计算机程序所能获得的信息能够用于提高计算机系统支路预测的准确性；4)为未来的计算机系统设计新的分支预测器，结合其他学科的技术，如机器学习，即研究计算机系统如何通过观察数据来学习。在这些领域中，将考虑到分支预测的技术限制。特别是，分支预测器必须非常迅速地行动，及时交付预测以提高性能，并且它应该以一种节能的方式这样做。这项研究将通过一个关于计算机系统研究和机器学习研究相互作用的特别研讨会带到课堂上。

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.