Introducing entropies for representing program behavior and branch predictor performance

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 ...

• Type: Conference
• Year: 2007
• Where: EXPCS
• Authors: Takashi Yokota, Kanemitsu Ootsu, Takanobu Baba
• Keywords: Branch Predictor, Branch Predictor Performance, EXPCS 2007, Prediction Performance, Theoretical Computer Science