Two-level adaptive training branch prediction
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
Analysis of branch prediction via data compression
Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
A Dynamic Periodicity Detector: Application to Speedup Computation
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
A study of branch prediction strategies
ISCA '81 Proceedings of the 8th annual symposium on Computer Architecture
Dynamic Branch Prediction with Perceptrons
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
The FAB Predictor: Using Fourier Analysis to Predict the Outcome of Conditional Branches
HPCA '02 Proceedings of the 8th International Symposium on High-Performance Computer Architecture
Multi-stage Cascaded Prediction
Multi-stage Cascaded Prediction
Limits of Indirect Branch Prediction
Limits of Indirect Branch Prediction
Piecewise Linear Branch Prediction
Proceedings of the 32nd annual international symposium on Computer Architecture
Introducing entropies for representing program behavior and branch predictor performance
Proceedings of the 2007 workshop on Experimental computer science
Understanding prediction limits through unbiased branches
ACSAC'06 Proceedings of the 11th Asia-Pacific conference on Advances in Computer Systems Architecture
| Hi-index | 0.00 |
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.