Abstract
The majority of currently available 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 quantify and evaluate the impact of unbiased branches and show that any gain in prediction accuracy is proportional to the frequency of unbiased branches. By using the SPECcpu2000 integer benchmarks we show that there are a significant proportion of unbiased branches which severely impact on prediction accuracy (averaging between 6% and 24% depending on the prediction context used).
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Burger, D., Goodman, J.R.: Billion Transistor Architectures. IEEE Computer, 46–49 (September 1997)
Chang, P.-Y., Hao, E., Yeh, T.-Y., Patt, Y.N.: Branch Classification: a New Mechanism for Improving Branch Predictor Performance. In: Proceedings of the 27th International Symposium on Microarchitecture, San Jose, California (1994)
Chappell, R., Tseng, F., Yoaz, A., Patt, Y.: Difficult-Path Branch Prediction Using Subordinate Microthreads. In: The 29th Annual International Symposia on Computer Architecture, Alaska, USA (May 2002)
Desmet, V., Eeckhout, L., De Bosschere, K.: Evaluation of the Gini-index for Studying Branch Prediction Features. In: Proceedings of the 6th International Conference on Computing Anticipatory Systems (CASYS). AIP Conference Proceedings, vol. 718, pp. 376–384 (2004)
Hennessy, J., Patterson, D.: Computer Architecture: A Quantitative Approach, 3rd edn. Morgan Kaufmann Publishers, San Francisco (2003)
Jiménez, D.A., Lin, C.: Dynamic Branch Prediction with Perceptrons. In: Proceedings of the 7th International Symposium on High Performance Computer Architecture (January 2001)
Loh, G.H.: Simulation Differences Between Academia and Industry: A Branch Prediction Case Study. In: International Symposium on Performance Analysis of Software and Systems (ISPASS), Austin, TX, USA, pp. 21–31 (March 2005)
McFarling, S.: Combining Branch Predictors, WRL Technical Note TN-36, Digital Equipment Corporation (June 1993)
Pan, S.T., So, K., Rahmeh, J.T.: Improving the accuracy of dynamic branch prediction using branch correlation. In: Proceedings of ASPLOS V, Boston, MA, pp. 76–84 (October 1992)
Patt, Y.N., Patel, S.J., Friendly, D.H., Stark, J.: One Billion Transistors, One Uniprocessor, One Chip. IEEE Computer 1, 51–57 (1997)
Simplescalar The SimpleSim Tool Set, ftp://ftp.cs.wisc.edu/pub/sohi/Code/simplescalar
SPEC, The SPEC benchmark programs, http://www.spec.org
Yeh, T.Y., Patt, Y.N.: Two-level adaptive branch prediction. In: Proceedings of the 24-the ACM/IEEE International Symposium on Microarchitecture (November 1991)
Vintan, L., Egan, C.: Extending Correlation in Branch Prediction Schemes. In: International Euromicro 1999 Conference, Italy (September 1999)
Vintan, L., Iridon, M.: Towards a High Performance Neural Branch Predictor. In: International Joint Conference on Neural Networks, Washington DC, USA (July 1999)
The 1st JILP Championship Branch Prediction Competition (CBP-1) (2004), http://www.jilp.org/cbp
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© 2006 Springer-Verlag Berlin Heidelberg
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Vintan, L., Gellert, A., Florea, A., Oancea, M., Egan, C. (2006). Understanding Prediction Limits Through Unbiased Branches. In: Jesshope, C., Egan, C. (eds) Advances in Computer Systems Architecture. ACSAC 2006. Lecture Notes in Computer Science, vol 4186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11859802_47
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DOI: https://doi.org/10.1007/11859802_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40056-1
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