Gang Luo, Hongfei Guo {gangluo, guo}@cs.wisc.edu Software-based and Hardware-based Branch Prediction Strategies and Performance Evaluation Gang Luo, Hongfei Guo {gangluo, guo}@cs.wisc.edu

Contents Background Implementation based on SimpleScalar3.0 Simulation Model Simulation Results Conclusion and Future work

1. Background Why branch prediction? Software-based branch predictors Hardware-based branch predictors

Why need branch prediction Avoid control hazard Predict branch direction and branch address Avoid stopping pipeline Speedup program execution

Classification of Branch Prediction Strategies Software-based static branch predictors Hardware-based dynamic branch predictors

Software-based Strategy Always Taken Always Not Taken Backward Taken, Forward Not Taken All branches with certain operation codes Taken, others Not Taken

Hardware-based Strategy One-bit counter Two-bit counter Gag Pag Pap Branch Instruction Table

2. Implementation based on SS3.0 Backward taken & forward not taken one-bit prediction counter fix a bug ---- Support command line configuration

current branch address Backward taken & forward not taken Y target address Current address > Target address Predicted address Next inst. address target address N Backward taken & forward not taken branch predictor

current branch address Predicted inst. address One bit counter (two bits counter) HASH table current branch address HASH 1 …… Lookup Predicted direction BTB update address2 target address2 Addressn-1 target address n-1 address1 target address1 …… address n target address n Predicted target 2 to 1 MUX Predicted inst. address Next inst. address Compare update New inst. address Branch prediction counter

3. Simulation Model Mode Level 1 Table Level 2 Table # of Entries Width of SR # of Entries One-bit -- -- 2048 Bimode -- -- 2048 GAg 1 11 2048 PAg 4 11 2048 PAp 4 7 2048 BTB 512 Associ. 4 --

Spec95 Benchmarks Used Gcc Compress Li M88k Perl

4. Simulation Results Results of software-based Strategies Results of hardware-based Strategies Software-based Strategies vs. hardware-based Strategies Explanations

Performance of Software-based Strategies

Analysis of Software-based Strategies Always Taken is the best one Always Not Taken & Backward Taken, Forward Not Taken are equally bad

Explanation Backward Taken, Forward Not Taken is mainly pointed at the for loop and while loop statement Most branches in the program are inclined to be taken

Performance of Hardware-based Strategies

Analysis of Hardware-based Strategies Two-bit counter and Gag are the best two One-bit counter, Pag, and Pap also achieve high prediction accuracy

Explanation One-bit counter: intrinsic shortcoming Pag: all the branch histories share the same global two-bit counter tables, which leads to confusion Pap: too many branch history pattern tables compared to the number of instructions executed, can’t get enough training

Software-based Strategies vs. Hardware-based Strategies Hardware-based strategies always achieve high prediction accuracy, greater than 90% Most software-based strategies achieve low prediction accuracy, less than 70%

Explanation Different data sets will let the programs have different dynamic branch behaviors Even for the same data set, the same branch instruction will exhibit different dynamic branch behaviors at different times in the same running

5. Future Work Use some AI algorithms to adjust the parameters of the branch prediction strategies, then we can ensure the most proper training time to achieve the highest performance.