Abstract
Predictors essentially predicts the most recent events based on the record of past events, history. It is obvious that prediction performance largely relies on regularity–randomness level of the history. This paper concentrates on extracting effective information from branch history, and discusses expected performance of branch predictors. For this purpose, this paper introduces entropy point-of-views for quantitative characterization of both program behavior and prediction mechanism. This paper defines four new entropies from different viewpoints; two of them are independent of prediction methods and the others are dependent on predictor organization. These new entropies are useful tools for analyzing upper-bound of prediction performance. This paper shows some evaluation results of typical predictors.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423, 623–656 (1948)
The 1st JILP Championship Branch Prediction Competition (2004), http://www.jilp.org/cbp/
The 2nd JILP Championship Branch Prediction Competition (2006), http://camino.rutgers.edu/cbp2/
Smith, J.E.: A Study of Branch Prediction Strategies. In: Proc. 8th Int’l Symp. Computer Architecture, pp. 135–148 (May 1981)
Yeh, T.-Y., Patt, Y.N.: Two-Level Adaptive Branch Prediction. In: Proc. 24th ACM/IEEE Int’l Symp. Microarchitecture, pp. 51–61 (November 1991)
McFarling, S.: Combining Branch Predictors, Technical Report TN–36, Digital Equipment Corp., Western Research Laboratory (June 1993)
Jiménez, D.A., Lin, C.: Dynamic Branch Prediction with Perceptrons. In: Proc. 7th Int’l Symp. High-Performance Computer Architecture, pp. 197–206 (January 2001)
Jiménez, D.A.: Piecewise Linear Branch Prediction. In: Proc. 32nd Annual Int’l Symp. Computer Architecture, pp. 382–393 (2005)
SimpleScalar LLC, http://www.simplescalar.com/
Standard Performance Evaluation Corporation, SPEC CPU2000 V1.3 (2004), http://www.spec.org/cpu2000/
Tyson, G., Lick, K., Farrens, M.: Limited Dual Path Execution, Technical Report CSE–TR–346–97, University of Michigan (1997)
Kise, K., et al.: The Bimode++ Branch Predictor Using the Feature of Extremely Biased Branches. IPSJ SIG Techical Report 2005(7), 57–62 (2005)
Freitag, F., Corbalan, J., Labarta, J.: A Dynamic Periodicity Detector: Application to Speedup Computation. In: Proc. 15th Int’l Parallel and Distributed Processing Symp. (April 2001)
Kampe, M., Stenstrom, P., Dubois, M.: The FAB Predictor: Using Fourier Analysis to Predict the Outcome of Conditional Branches. In: Proc. 8th Int’l Symp. High-Performance Computer Architecture, pp. 223–232 (February 2002)
Chen, I-C.K., Coffey, J.T., Mudge, T.N.: Analysis of Branch Prediction via Data Compression. In: Proc. 7th Int’l Conf. Architectural Support for Programming Languages and Operating Systems, pp. 128–137 (October 1996)
Mudge, T., Chen, I.-C., Coffey, J.: Limits to Branch Prediction, Technial Report CSE–TR–282–96, University of Michigan (February 1996)
Driesen, K., Hölzle, U.: Limits of Indirect Branch Prediction, Technical Report TRCS97–10, Computer Science Department, University of California, Santa Barbara (June 1997)
Driesen, K., Hölzle, U.: Multi-stage Cascaded Prediction, Technical Report TRCS99–05, Computer Science Department, University of California, Santa Barbara (February 1999)
Jha, S., Lu, Y., Clarke, E.: Formal Analysis of Branch Prediction Algorithm, Technical Report, Computer Science, Carnegie Mellon University (1998)
Vintan, L., et al.: Understanding Prediction Limits Through Unbiased Branches. In: Jesshope, C., Egan, C. (eds.) ACSAC 2006. LNCS, vol. 4186, pp. 480–487. Springer, Heidelberg (2006)
Yokota, T., et al.: Entropy Properties in Program Behaviors and Branch Predictors. In: Proc. 18th IASTED Int’l Conf. Parallel and Distributed Computing and Systems, pp. 448–453 (November 2006)
Yokota, T., Ootsu, K., Baba, T.: Introducing Entropies for Representing Program Behavior and Branch Prediction Performance. In: Proc. 2007 Workshop on Experimental Computer Science, ACM digital library (June 2007)
Yokota, T., Ootsu, K., Baba, T.: Proposal of Entropies for Representing Program Behavior and Branch Prediction Performance. IPSJ Transactions on Advanced Computing Systems (to appear)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yokota, T., Ootsu, K., Baba, T. (2008). Potentials of Branch Predictors: From Entropy Viewpoints. In: Brinkschulte, U., Ungerer, T., Hochberger, C., Spallek, R.G. (eds) Architecture of Computing Systems – ARCS 2008. ARCS 2008. Lecture Notes in Computer Science, vol 4934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78153-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-78153-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78152-3
Online ISBN: 978-3-540-78153-0
eBook Packages: Computer ScienceComputer Science (R0)