Selective Branch Prediction Reversal by Correlating with Data Values and Control Flow

Branch prediction is one of the main hurdles in the roadmap towards higher clock frequencies and deeper pipelines. This work presents a new approach to enhancing current branch predictors: Selective Branch Prediction Reversal. The rationale behind this proposal is the fact that many branch mispredictions can be avoided if branch prediction is selectively reversed. We present a Branch Prediction Reversal Unit (BPRU) that selectively reverses branch predictions by correlating with the predicted values of the branch inputs, in addition to recent control flow. As a case study, we have included the BPRU in an already proposed branch predictor, the Branch Predictor through Value Prediction (BPVP). The effect is a reduction by half in its original misprediction rate. We have also measured the improvement when the BPRU is used in a hybrid scheme composed of a BPVP and a gshare predictors. Results using immediate updates show average reductions in misprediction rate ranging from 7% to 14%. Performance evaluation of the proposed BPRU in a 20-stage superscalar processor shows an IPC improvement of up to 9%

Selective branch prediction reversal by correlating with data values and control flow

Branch prediction is one of the main hurdles in the roadmap towards deeper pipelines and higher clock frequencies. This work presents a new approach to enhancing current branch predictors: Selective Branch Prediction Reversal. The rationale behind this proposal is the fact that many branch mispredictions can be avoided if branch prediction is selectively reversed. We present a Branch Prediction Reversal Unit (BPRU) that selectively reverses branch predictions by correlating with the predicted values of the branch inputs, in addition to recent control flow. As a case study, we have included the BPRU in an already proposed branch predictor, the Branch Predictor through Value Prediction (BPVP). The effect is a reduction by half in its original misprediction rate. We have also measured the improvement when the BPRU engine is used in a hybrid scheme composed of a BPVP and a gshare predictor. Results using immediate updates show average reductions in misprediction rate ranging from 7% to 14%. Performance evaluation of the proposed BPRU in a 20-stage superscalar processor shows an IPC improvement of up to 9%.Peer Reviewe

Performance Limitations in Wide Superscalar Processors

Superscalar processors with wide instruction fetch only results in diminishing performance returns. The aim of this research to find what causes these limitations. In addition, a new cycle-accurate computer architecture simulator - AbaKus - is developed to study and evaluate the performance of the architecture designs. Eager-Based executions and their designs are tested to overcome the effects of low-accuracy of branch prediction on 38% of the conditional branch instructions. An improvement IPC of 27% on average is shown. However, confidence estimators need improvement on its design logic as they prove critical on the performance of eager-based executions. In addition, the limitation of compilers to extract ILP from the benchmark programs leads to a severe restriction on performance of Superscalar architectures due to data dependencies.School of Electrical & Computer Engineerin