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
Assigning confidence to conditional branch predictions
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
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
Perceptron-Based Branch Confidence Estimation
HPCA '04 Proceedings of the 10th International Symposium on High Performance Computer Architecture
Two-Path Limited Speculation Method for Static/Dynamic Optimization in Multithreaded Systems
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
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Predictors are inevitable components in the state-of-the-art microprocessors and branch predictors are actively discussed from many aspects. Performance of a branch predictor largely depends on the dynamic behavior of the executing program, however, we have no effective metrics to represent the nature of program behavior quantitatively. In this paper, we introduce an information entropy idea to represent program behavior and branch predictor performance. By simple application of Shannon's information entropy, we introduce new entropy, Source Entropy, that quantitatively represents the regularity level of program behavior. We show that the entropy also represents prediction performance independent of prediction mechanisms. We further discuss stereoscopic view of branch predictor performance from two entropy viewpoints, and introduce two entropies, Reference Entropy and Transition Entropy. The latter entropy offers theoretically maximum prediction performance when a predictor has table-based organization. We evaluated the proposed three entropies and prediction performance in various situations. Artificially generated branch patterns, as preliminary experiments, show overview of the entropies and prediction performance. Comparison with 2nd Championship Branch Predictor competition results show high potential of our entropy. Finally, application results to SPEC CPU2000 benchmarks show actual view of our entropies and prediction performance.