An Efficient WCET-Aware Hybrid Global Branch Prediction Approach
Date of Original Version
We investigate the problem of reducing the number of branch mispredictions for a task such that its WCET (Worst-Case Execution Time) is minimized, and propose a novel branch correlation-based, hybrid branch prediction approach. Our approach consists of a static profile-based branch correlation analyzer and a dynamic branch predictor. The static profile-based branch correlation analyzer uses profiling and data dependency analysis to find precise correlations between branches and identifies all the branches that do not have any impact on the WCET of the task. The dynamic predictor uses the correlation information to make online predictions. We have implemented our approach and compared it with the two state-of-the-art branch prediction approaches by using a set of benchmark suite. The experimental results show that our approach outperforms the two state-of-theart approaches. The maximum WCET improvement and the average WCET improvement of our approach over the WCETaware static branch prediction approach are 41.35% and 11.40%, respectively. The maximum WCET improvement and the average WCET improvement of our approach over the global dynamic branch prediction approach are 32.41% and 7.67%, respectively.
Proceedings - 2016 IEEE 22nd International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2016
Su, Xuesong, Hui Wu, and Qing Yang. "An Efficient WCET-Aware Hybrid Global Branch Prediction Approach." Proceedings - 2016 IEEE 22nd International Conference on Embedded and Real-Time Computing Systems and Applications, RTCSA 2016 , (2016): 195-201. doi:10.1109/RTCSA.2016.46.