1. Aliasing and Anti-Aliasing in Branch History Table Prediction
Kris Breen
EE 710 A2
Nov. 26, 2002

2. OUTLINE Introduction Theory Experiment Results
Conclusions & Future Work
Aliasing and Anti-Aliasing in BHT Prediction

3. OUTLINE Introduction Theory Experiment Results
Conclusions & Future Work
Aliasing and Anti-Aliasing in BHT Prediction

4. INTRODUCTION Branch history predictors have advantage of simplicity
Prediction accuracy limited by table size, due to aliasing
Aliasing and Anti-Aliasing in BHT Prediction

5. INTRODUCTION 2n-entry BHT n Hash PC of branch Function
Aliasing and Anti-Aliasing in BHT Prediction

6. INTRODUCTION 2n-entry BHT PC of branch 1 n Hash Function
ALIASING
PC of branch 2
Aliasing and Anti-Aliasing in BHT Prediction

7. OUTLINE Introduction Theory Experiment Results
Conclusions & Future Work
Aliasing and Anti-Aliasing in BHT Prediction

8. THEORY What performance loss occurs due to aliasing?
Can reasonably assume that aliasing has a negative impact on branch prediction.
Start by assuming that hash function simply uses low bits of address.
Analyze static program properties first.
Aliasing and Anti-Aliasing in BHT Prediction

9. THEORY m = total number of addresses in program
size of predictor table
pb = probability that an instruction is a branch
p(0 branches hash to x) = (1 - pb)m
p(2 or more branches hash to x) =
1 - p(0 branches hash to x)- p(1 branch hashes to x)
= 1 - (1 - pb)m - pb(1 - pb)m-1
Aliasing and Anti-Aliasing in BHT Prediction

10. THEORY
Assuming 1 in 7 instructions is a branch, and a program size 10 times larger than the table size,
p(2 or more branches hash to x) = 71%
For a 1024 entry table, this means that about 700 entries are experiencing aliasing. Further evaluation shows that of the remaining 300 entries, about 250 of them likely have no branches hashing to them. These can be used for anti-aliasing.
Aliasing and Anti-Aliasing in BHT Prediction

11. OUTLINE Introduction Theory Experiment Results
Conclusions & Future Work
Aliasing and Anti-Aliasing in BHT Prediction

12. EXPERIMENT
Used SimpleScalar to analyze effects of aliasing in programs
Based experiments on PISA architecture
Used 2-bit predictor and hash function based on low bits of address
Aliasing and Anti-Aliasing in BHT Prediction

13. EXPERIMENT
Modified bpred.[ch] to remember entire branch PC when BHT lookup is done.
On every BHT lookup, bpred.c compares the current branch PC to the stored branch PC. If these differ, then a lookup alias has occurred, and it is recorded.
On BHT update, bpred.c checks if the prediction was correct. If it wasn’t, and it was from an aliased table entry, a miss due to aliasing is recorded.
Aliasing and Anti-Aliasing in BHT Prediction

14. EXPERIMENT
Simulations were run using Todd Austin’s SPEC2000-derived benchmarks (gcc, anagram, compress, go)
Branch history table sizes of 32, 256, 1024, and 8192 were each used for each benchmark.
Percentage of aliased lookups and percentage of misses due to aliasing were recorded for each simulation.
Aliasing and Anti-Aliasing in BHT Prediction

15. OUTLINE Introduction Theory Experiment Results
Conclusions & Future Work
Aliasing and Anti-Aliasing in BHT Prediction

16. RESULTS Sample simulation output (from gcc using a 1024 entry table):
bpred_bimod.lookups # total number of bpred lookups
bpred_bimod.dir_hits # total number of direction-predicted hits
bpred_bimod.misses # total number of misses
bpred_bimod.aliases # total number of aliases
bpred_bimod.alias_rate # alias rate (aliases/lookups)
bpred_bimod.alias_misses # number of mispredictions due to aliasing
Aliasing and Anti-Aliasing in BHT Prediction

17. RESULTS
Aliasing and Anti-Aliasing in BHT Prediction

18. RESULTS
Miss rate when using aliased BHT entries ranged from 44% to 61%, which supports concept that aliased accesses provide effectively random information
Aliasing and Anti-Aliasing in BHT Prediction

19. OUTLINE Introduction Theory Experiment Results
Conclusions & Future Work
Aliasing and Anti-Aliasing in BHT Prediction

20. CONCLUSIONS
Reduction of aliasing can provide a substantial performance gain at minimal cost when using BHT
Worst case miss rate =
predictor miss rate + alias rate
For a predictor miss rate of 10% and an alias rate of 10%, halving the alias rate improves overall prediction by 25%
Aliasing and Anti-Aliasing in BHT Prediction

21. FUTURE WORK
Attempt to reduce loss due to aliasing by passing hash information to processor from program
Determine program properties (pb, m) for which anti-aliasing will be effective
Aliasing and Anti-Aliasing in BHT Prediction

22. fin
Aliasing and Anti-Aliasing in BHT Prediction