Online Ensemble Learning: An Empirical Study
Machine Learning
Alloyed branch history: combining global and local branch history for robust performance
International Journal of Parallel Programming
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Abstract: We present a new method for branch prediction that encodes in the branch instruction a formula, chosen by profiling, that is used to perform history-based prediction. By using a special class of Boolean formulas, our encoding is extremely concise. By replacing the large tables found in current predictors with a small, fast circuit, our scheme is ideally suited to future technologies that will have large wire delays. In a projected 70 nm technology and an aggressive clock rate of about 5 GHz, an implementation of our method that uses an 8-bit formula encoding has a misprediction rate of 6.0%, 42% lower than that of the best gshare predictor implementable in that same technology. In today's technology, a 16-bit version of our predictor can replace bias bits in an 8K-entry agree predictor to achieve a 2.86% misprediction rate, which is slightly lower than the 2.93% misprediction rate of the Alpha 21264 hybrid predictor, even though the Alpha predictor has almost twice the hardware budget. Our predictor also consumes much less power than table-based predictors. This paper describes our predictor, explains our profiling algorithm, and presents experimental results using the SPEC 2000 integer benchmarks.