1. Combining Branch Predictors
CS Lecture 7
Combining Branch Predictors
Scott McFarling
WRL Tech. Report TN-36
1993

2. Bimodal Branch Prediction
Identifies most popular prediction in recent past
Updates happen during commit 1 PC
10-bit index
1024 entries
2-bit saturating
counters

3. Results SPEC’89 programs simulated for 10M instrs
(modern studies use hard-to-predict programs)
A larger predictor reduces contention for counters
Prediction rates saturate at 93.5% (at 2K bytes)
(Fig.3)

4. Local Predictors Two-Level predictor: The first level has history,
the second level has saturating counters
History gets updated immediately 1 1 1 PC 1
10-bit index
16 entries
1024 entries
2-bit saturating
counters
4-bit history table

5. Results For small predictors, there could be contention
at both levels, resulting in inaccurate predictions
Will also take longer to warm up – after every
context switch
Does very well for large predictors – saturates at
97.1%

6. Global Predictors A single history register – neighboring branches
have correlated results
However, the PC is not used 1
1024 entries
10-bit global history
2-bit saturating
counters

7. Do We Need PC? Note that the global history reveals which branch
is being examined
Hence, it outdoes bimodal predictors when the
transistor budget is large (Fig.7)
Local predictor does better – it is more important
to identify the PC and local history than behavior
of neighboring branches

8. Gselect Use a combination of PC and global history
Bimodal and global prediction are special cases
(Fig.9) 1 n PC /
n+m / /
1024 entries m
5-bit global history
2-bit saturating
counters

9. GShare Xor-ing 10 history bits and 10 PC bits has more
info than the concatenation of 5 bits of each and
more info than each individual component
Branch
Address
Global
History
Gselect
4/4
Gshare
8/8

10. Terminology GAG: Global history indexes into global array
of saturating counters
PAG: Per-address history indexes into global array
GAP: Global history indexes into each PC’s private
array of counters (gselect)
PAP: Per-address history indexes into each PC’s
private array of counters

11. Trade-Offs Some predictors warm-up faster than others
Some programs benefit from global history, some
from local history
Some programs have branches that interfere
with each other
Note that a 64KB local predictor has fewer
saturating counters than a 64KB bimodal predictor
– the former won’t be better for every program

12. Combining Predictors Use an array of saturating counters to pick the
best available predictor for each PC
Predictor A 1 PC
1024 entries
Predictor B
2-bit saturating
counters

13. Results The combination of local and gshare increases
the prediction accuracy to 98.1% (Fig.16)
For smaller transistor budgets, the combination
of bimodal and gshare is better (gshare is twice
the size to make sure the total is a power of two)
A 1KB combined predictor does as well as a
16KB gselect predictor

14. Future Work Detect conflicts, correlations, and common
predictions through profiling/compiler analysis
Functions that compress information in history
or PC
Pipeline predictions – predict two branches ahead
Hierarchical predictors – get a quick prediction in
a cycle and a more accurate one two cycles later

15. Next Week’s Paper “Design Trade-Offs for the Alpha EV8 Conditional
Branch Predictor”, Seznec et al., ISCA’02

16. Title
Bullet