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，以便未来指令的工作可以提前开始，从而减少程序运行所需的时间。 分支预测器必须高度精确，精确度的微小改进可以大大提高性能。 这个项目是继续研究分支预测的原则性方法。 将探索几种新的理解和改进分支预测的方法：1）通过开发一个理想的分支预测器模型，在合理的假设下，探索分支预测提高性能的潜力的极限;2）改进在真实的计算机系统上运行计算机程序的技术，使这些程序具有更好的分支预测精度;第三章发现改进计算机程序和计算机系统之间的通信的方式，使得计算机程序可用的信息可以用于改进通信。计算机系统中分支预测的准确性;以及4）致力于用于未来计算机系统的新的分支预测器设计，结合来自其他学科的技术，例如机器学习，即，研究计算机系统如何通过观察数据进行学习的学科。2在上述每一个领域，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行动，及时提供其预测以提高性能，并且它应该以节能的方式这样做。 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殊研讨班带入课堂。

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.