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 can be expected (asymptotically) from two-level predictors, since PPM is optimal. However, there are a rich set of predictors available from data compression, several of which can still yield some improvement in cases where resources are limited. To illustrate this, we conduct trace-driven simulation running the Instruction Benchmark Suite and the SPEC CINT95 benchmarks. The results show that PPM can outperform a two-level predictor for modest sized branch target buffers.