Introducing entropies for representing program behavior and branch predictor performance

摘要

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.