Abstract
Branch prediction reversal has been proved to be an effective alternative approach to dropping misprediction rates by means of adding a Confidence Estimator to a correlating branch predictor. This paper presents a Branch Prediction Reversal Unit (BPRU) especially oriented to enhance correlating branch predictors, such as the gshare and the Alpha 21264 metapredictor. The novelty of this proposal lies on the inclusion of data values in the confidence estimation process. Confidence metrics show that the BPRU can correctly tag 43% of branch mispredictions as low confident predictions, whereas the SBI (a previously proposed estimator) just detects 26%. Using the BPRU to reverse the gshare branch predictions leads to misprediction reductions of 15% for the SPECint2000 (up to 27% for some applications). Furthermore, the BPRU+gshare predictor reduces the misprediction rate of the SBI+gshare by an average factor of 10%. Performance evaluation of the BPRU in a superscalar processor obtains speedups of up to 9%. Similar results are obtained when the BPRU is combined with the Alpha 21264 branch predictor.
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Aragón, J.L., González, J., García, J.M., González, A. (2001). Confidence Estimation for Branch Prediction Reversal. In: Monien, B., Prasanna, V.K., Vajapeyam, S. (eds) High Performance Computing — HiPC 2001. HiPC 2001. Lecture Notes in Computer Science, vol 2228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45307-5_19
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DOI: https://doi.org/10.1007/3-540-45307-5_19
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