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

target address

Current address >

Target address

target address

Next inst. address

Y

N

Predicted address

Backward taken & forward not taken branch predictor

update

Predicted direction

Lookup

HASH

address2 target address2

Addressn-

1

target address n-1

address1 target address1

……

address n target address n

0 1 …… 1 0

current branch address

Predicted target

BTB

2 to 1 MUX

Next inst. address

Predicted inst. address

Compare

New inst. address

update

Branch prediction counter

One bit counter (two bits counter)

HASH table

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

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

gcc compress li m88k perl

benchmark

perc

enta

ge o

f co

rrec

t br

anch

pre

dict

ion

taken

not taken

back taken

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

0.75

0.8

0.85

0.9

0.95

1

gcc compress li m88k perl

benchmark

perc

enta

ge o

f co

rrec

t br

anch

pre

dict

ion

one bit

two bit

Gag

Pag

Pap

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.