Data Compression Techniques for Branch Prediction

Without special handling branch instructions would disrupt the smooth flow of instructions into the microprocessor pipeline. To eliminate this disruption, many modern systems attempt to predict the outcome of branch instructions, and use this prediction to fetch, decode and even evaluate future instructions. Recently, researchers have realized that the task of branch prediction for processor optimization is similar to the task of symbol prediction for data compression. Substantial progress has been made in developing approximations to asymptotically optimal compression methods, while respecting the limited resources available within the instruction prefetching phase of the processor pipeline. Not only does the infusion of data compression ideas result in a theoretical fortification of branch prediction, it results in real and significant empirical improvement in performance, as well. We present an overview of branch prediction, beginning with early techniques through more recent data compression inspired schemes. A new approach is described which uses a non-parametric probability density estimator similar to the LZ77 compression scheme [23]. Results are presented comparing the branch prediction accuracy of several schemes with those achieved by our new approach.