Analysis of Branch Prediction Via Data Compression

Branch prediction is an important mechanism in modern microprocessor design. The focus of research in this area has been on designing new branch prediction schemes. In contrast, very few studies address the theoretical basis behind these prediction schemes. Knowing this theoretical basis helps us to evaluate how good a prediction scheme is and how much we can expect to improve its accuracy. In this paper, we apply techniques from data compression to establish a theoretical basis for branch prediction, and to illustrate alternatives for further improvement. To establish a theoretical basis, we first introduce a conceptual model to characterize each component in a branch prediction process. Then we show that current "two-level" or correlation based predictors are, in fact, simplifications of an optimal predictor in data compression, Prediction by Partial Matching (PPM). If the information provided to the predictor remains the same, it is unlikely that significant improvements ...

• Type: Conference
• Year: 1996
• Where: ASPLOS
• Authors: I-Cheng K. Chen, John T. Coffey, Trevor N. Mudge
• Keywords: ASPLOS 1996, Branch Prediction, Prediction Scheme, Programming Languages, Theoretical Basis