An Idealistic Neuro-PPM Branch Predictor

PMPM: Prediction by Combining Multiple Partial Matches

The prediction by partial matching (PPM) algorithm has been well known for its high prediction accuracy. Recent proposals of PPM-like predictors confirm its effectiveness on branch prediction. In this paper, we introduce a new branch prediction algorithm, named Prediction by combining Multiple Partial Matches (PMPM). The PMPM algorithm selectively combines multiple matches instead of using the ...

Neuro-PPM Branch Prediction

Historically, Markovian predictors have been very successful in predicting branch outcomes. In this work we propose a hybrid scheme that employs two Prediction by Partial Matching (PPM) Markovian predictors, one that predicts based on local branch histories and one based on global branch histories. The two independent predictions are combined using a neural network. On the CBP-2 traces the prop...

The Idealistic GTL Predictor

Advanced Techniques for Improving Indirect Branch Prediction Accuracy

Deep pipelines and fast clock rates are necessitating the development of high accuracy branch predictors. From microarchitectural viewpoint, in the last decade the importance of indirect branch prediction increased even though, in the computing programs the indirect jumps remain less frequent than the more predictable conditional branches. One reason refers to predicative execution that implies...

Predicting Indirect Branches via Data Compression

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 are better able to exploit Instruction Level Parallelism (ILP) by providing a larger window of instructions. However, when a branch is mispredicted, instructions from the misp...