Abstract
The majority of currently available dynamic branch predictors base their prediction accuracy on the previous k branch outcomes. Such predictors sustain high prediction accuracy but they do not consider the impact of unbiased branches, which are difficult-to-predict. In this paper, we evaluate the impact of unbiased branches in terms of prediction accuracy on a range of branch difference predictors using prediction by partial matching, multiple Markov prediction and neural-based prediction. Since our focus is on the impact that unbiased branches have on processor performance, timing issues and hardware costs are out of scope of this investigation. Our simulation results, with the SPEC2000 integer benchmark suite, are interesting even though they show that unbiased branches still restrict the ceiling of branch prediction and therefore accurately predicting unbiased branches remains an open problem.
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Gellert, A., Florea, A., Vintan, M., Egan, C., Vintan, L. (2007). Unbiased Branches: An Open Problem. In: Choi, L., Paek, Y., Cho, S. (eds) Advances in Computer Systems Architecture. ACSAC 2007. Lecture Notes in Computer Science, vol 4697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74309-5_4
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DOI: https://doi.org/10.1007/978-3-540-74309-5_4
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