1. Looking for limits in branch prediction with the GTL predictor
André Seznec
IRISA/INRIA/HIPEAC

2. Motivations
Geometric history length predictors introduced in
OGEHL, CBP-1, dec. 2004
TAGE, JILP ’06, feb. 2006
Storage effective
Exploits very long global histories
Were defined with possible implementation in mind
What are the limits of accuracy that can be captured with these schemes ?
How do they compare with unconstrained prediction schemes ?

3. Geometric history length predictors: global history +multiple lengths?
L(0) T3
L(1)
L(2) T4
L(3)
L(4)

4. GEometric History Length predictor
The set of history lengths forms a geometric series
Capture correlation
on very long histories
{0, 2, 4, 8, 16, 32, 64, 128}
most of the storage
for short history !!
What is important: L(i)-L(i-1) is drastically increasing

5. Combining multiple predictions
Neural inspired predictors
Use a (multiply)-add tree
Partial matching
Use tagged tables and the longest matching history
O-GEHL, CBP-1
TAGE, JILP’ 06

6. Final computation through a sum 256Kbits: 12 components 3.670 misp/KI
CBP-1 (2004): O-GEHL
Final computation through a sum ∑
L(4)
L(3)
L(2)
L(1) TO T1 T2 T3 T4
L(0)
Prediction=Sign
256Kbits: 12 components misp/KI

7. JILP ‘06: TAGE longest matching history=? =? =? 1 1 1 1 1 1 1 1 1
256Kbits: misp/KI

8. What is global history conditional branch history:
path confusion on short histories 
path history:
Direct hashing leads to path confusion 
Represent all branches in branch history
Use path AND direction history

9. Using a kernel history and a user history
Traces mix user and kernel activities:
Kernel activity after exception
Global history pollution
Solution: use two separate global histories
User history is updated only in user mode
Kernel history is updated in both modes

10. Accuracy limits for TAGE
Varying the predictor size, the number of components, the tag width, the history length.
Allowing multiple allocations
The best accuracy on distributed traces:
3.054 misp/KI
History length around 1,000
15-20 components
No need for tags wider than 16 bits

11. Accuracy limits for GEHL
Varying the predictor size, the number of components, the history length, counter width
(slightly) improving the update policy
and fitting in the two hours simulation rule 
on the distributed traces:
2.842 misp/KI
97 components
8 bits counter
2,000 bits global history

12. GEHL vs TAGE
Realistic implementation parameters (storage budget, number of components)
TAGE is more accurate than (O-)GEHL
Unlimited budget, huge number of components
GEHL is more accurate than TAGE

13. Will it be sufficient to win The Championship ?
GEHL history length: 2,000
97 components
2.842 misp/KI

14. A step further: hybrid GEHL-TAGE
On a few benchmarks, TAGE is more accurate than GEHL,
Let us try an hybrid GEHL-TAGE predictor

15. Branch/path history + PC
Hybrid GEHL-TAGE
Branch/path history + PC
GEHL
TAGE
Meta =
egskew
mux
Inherit from:
Agree/bimode, YAGS, 2bcgskew,

16. GEHL+TAGE GEHL provides the main prediction:
also used as the base predictor for TAGE
(YAGS inspired)
TAGE records when GEHL fails:
{prediction, address, history}
(agree/bimode, YAGS inspired)
Meta selects between GEHL and TAGE
(2bcgskew inspired)

17. Let us have fun !! GEHL history length: 400
TAGE history length: 100,000
2.774 misp/KI

18. Might still be unsufficient 
GEHL history length: 400
TAGE history length: 100,000
2.774 misp/KI

19. Adding a loop predictor
The loop predictor captures the number of iterations of a loop
When successively encounters 8 times the same number of iterations, the loop predictor provides the prediction.
Advantage:
Very reliable

20. Branch/path history + PC
GTL predictor
Branch/path history + PC
GEHL
TAGE
Meta =
egskew
mux
Loop
predictor
confidence
+ static prediction on first occurrence

21. Hope this will be sufficient
to win
the Championship !!
GTL
GEHL, 97 comp., 400 hist.
+ TAGE, 19 comp., 100,000 hist
+ loop predictor
2.717 misp/KI

22. Geometric History Length predictors and limits on branch prediction
Unlimited budget, huge number of components
GEHL is more accurate than TAGE
Very old correlation can be captured:
On two benchmarks, using 10,000 history is really helping
Does not seem to be a lot of potential extra benefit from local history
We did not find any interesting extra scheme apart loop prediction
Loop prediction, very marginal apart gzip

23. The End 