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
A Mathematical Theory of Communication
A Mathematical Theory of Communication
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
Entropy representation of memory access characteristics and cache performance
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Potentials of branch predictors: from entropy viewpoints
ARCS'08 Proceedings of the 21st international conference on Architecture of computing systems
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Predictors are inherent components of state-of-the-art microprocessors. Branch predictors are discussed actively from diverse perspectives. Performance of a branch predictor largely depends on the dynamic behavior of the executing program. Nevertheless, 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. Through simple application of Shannon's information entropy, we introduce new entropy, Branch History Entropy, which quantitatively represents the regularity level of program behavior. We show that the entropy also represents an index of prediction performance that is independent of prediction mechanisms. We further discuss branch predictor performance from a stereoscopic view of their typical organization. We propose two entropies: Table Reference Entropy and Table Entry Entropy. The former represents an unbalanced level of references of table entries. The latter offers the maximum expectation in prediction performance. We evaluated the proposed three entropies and prediction performance in various situations. Artificially generated branch patterns, as preliminary experiments, show an overview of the entropies and prediction performance. Subsequently, we present a comparison to the 2nd Championship Branch Predictor competition results and show the high potential of the proposed entropy. Finally, we present an actual view of our entropies and prediction performance as application results to SPEC CPU2000 benchmarks.