Two-level adaptive training branch prediction

Abstract

High-performance microarchitectures use, among other structures, deep pipelines to help speed up execution. The importance of a good branch predictor to the effectiveness of a deep pipeline in the presence of conditional branches is well-known. In fact, the literature contains proposals for a number of branch prediction schemes. Some are static in that they use opcode information and profiling statistics to make predictions. Others are dynamic in that they use run-time execution history to make predictions. This paper proposes a new dynamic branch predictor, the Two-Level Adaptive Training scheme, which alters the branch prediction algorithm on the basis of information collected at run-time. Several configurations of the Two-Level Adaptive Training Branch Predictor are introduced, simulated, and compared to simulations of other known static and dynamic branch prediction schemes. Two-Level Adaptive Training Branch Prediction achieves 97 percent accuracy on nine of the ten SPEC benchmarks, compared to less than 93 percent for other schemes. Since a prediction miss requires flushing of the speculative execution already in progress, the relevant metric is the miss rate. The miss rate is 3 percent for the Two-Level Adaptive Training scheme vs. 7 percent (best case) for the other schemes. This represents more than a 100 percent improvement in reducing the number of pipeline flushes required.