2019 IEEE International Symposium on Workload Characterization (IISWC) 2019
DOI: 10.1109/iiswc47752.2019.9042108
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Branch Prediction Is Not A Solved Problem: Measurements, Opportunities, and Future Directions

Abstract: Modern branch predictors predict the vast majority of conditional branch instructions with near-perfect accuracy, allowing superscalar, out-of-order processors to maximize speculative efficiency and thus performance. However, this impressive overall effectiveness belies a substantial missed opportunity in single-threaded instructions per cycle (IPC). For example, we show that correcting the mispredictions made by the state-ofthe-art TAGE-SC-L branch predictor on SPECint 2017 would improve IPC by margins simila… Show more

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Cited by 13 publications
(10 citation statements)
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“…This storage efficiency issue is in line with previous work that exposed it by examining TAGE-SC-L 64KB with large code-footprint applications [22]. Storing only sparse correlations could therefore generally improve the performance of state-of-the-art branch predictors by eliminating the need to represent irrelevant features in their storage, thereby reducing the predictor's footprint.…”
Section: Sparse Branch Correlationssupporting
confidence: 76%
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“…This storage efficiency issue is in line with previous work that exposed it by examining TAGE-SC-L 64KB with large code-footprint applications [22]. Storing only sparse correlations could therefore generally improve the performance of state-of-the-art branch predictors by eliminating the need to represent irrelevant features in their storage, thereby reducing the predictor's footprint.…”
Section: Sparse Branch Correlationssupporting
confidence: 76%
“…4. Our proposal assumes a deployment scenario where predictions are generated at runtime after an offline training phase, as also considered in the work of Lin & Tarsa [22]. Offline training is necessary to capture the predictive statistics of the otherwise hardly detectable sparse correlations.…”
Section: Sparse Predictor Architecturementioning
confidence: 99%
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“…Recent work [12] shows that even though the current stateof-the-art branch predictors have almost perfect prediction accuracy, there is scope for gaining significant performance by fixing the remaining mispredictions. The core architecture could be tuned to be wider if it had the support of better branch prediction, which could potentially offer more IPC gains.…”
Section: Introductionmentioning
confidence: 99%