Predicting indirect branches via data compression

Abstract

Branch prediction is a key mechanism used to achieve high performance on multiple issue, deeply pipelined processors. By predicting the branch outcome at the instruction fetch stage of the pipeline, superscalar processors become able to exploit Instruction Level Parallelism (ILP) by providing a larger window of instructions. However, when a branch is mispredicted, instructions from the mispredicted path must be discarded. Therefore, branch prediction accuracy is critical to achieve high performance. Existing branch prediction schemes can accurately predict the direction of conditional branches, but have difficulties predicting the correct targets of indirect branches. Indirect branches occur frequently in Object-Oriented Languages (OOL), as well as in Dynamically-Linked Libraries (DLLs), two programming environments rapidly increasing in popularity. In addition, certain language constructs such as multi-way control transfers (e.g., switches), and architectural features such as 64-bit address spaces, utilize indirect branching. In this paper, we describe a new algorithm for predicting unconditional indirect branches called Prediction by Partial Matching (PPM). We base our approach on techniques proven to work optimally in the field of data compression. We combine a viable implementation of the PPM algorithm with dynamic per-branch selection of path-based correlation and compare its prediction accuracy against a variety of predictors. Our results show that, for approximately the same hardware budget, the combined predictor can achieve a misprediction ratio of 9.47%, as compared to 11.48% for the previously published most accurate indirect branch predictor.