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，以便可以提早开始处理未来的指令，从而减少程序运行所需的时间。分支预测器必须非常准确，精度稍有提高就能为性能带来很大好处。本项目是继续分支预测研究的一种原则性方法。将探索几种理解和改进分支预测的新方法：1)通过开发给出合理假设的理想分支预测器的模型来探索分支预测改进性能的潜力的极限；2)改进用于在实际计算机系统上运行计算机程序的技术，使得这些程序将具有更好的分支预测精度；3)发现改进计算机程序和计算机系统之间的通信的方法，使得计算机程序可用的信息可以用于提高计算机系统中的分支预测的精度；以及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学习。在每个领域，分支预测的技术限制都将被考虑在内。特别是，分支预测器必须非常迅速地采取行动，及时提供预测以提高性能，并且应该以一种节能的方式做到这一点。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的互动的特别研讨会课程带到课堂上。

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.