Exploring Branch Prediction
Aditya Akella
Shuchi Chawla
Jia-Yu Pan
A Framework for Branch Prediction
Dividing Streams
• First level: per branch substream
• Finer decomposition
– Branch pattern history
– Global pattern history
– Global path history
Loop1:
addq r1, r2, r3
addq r2, r4, r5
loop 2:
subq r2,r3,r5
beq r5, loop2
bne r5, loop1
Executes twice
1011…
1110…
(b1,1)(b2,1)(b2,1)…
Path vs Pattern
• As expected, path history
provides better correlation
Predictors
• Static vs Dynamic
Biased stream:
Majority prediction
• Adapt to changing bias
• Damping: 2 (or more) – bit predictor
• Is more better?
Static is better on a large number of branches
But, dynamic far outperforms static on the rest
Adaptive schemes should exploit this
Implementation Issues: Limited space
• Many branches map to same stream: Aliasing
• Mostly a destructive effect
• More aggressive schemes may not be necessarily better
gshare
GAs
6 bits branch identifier
6 bits history
12 bits branch identifier

concatenate
12 bit history
12 bits stream identifier
12 bits stream identifier
truncate to 1024 streams
truncate to 1024 streams
Implementation Issues: Limited space
• Many branches map to same stream: Aliasing
• Mostly a destructive effect
• More aggressive schemes may not be necessarily better
Cross-procedure Correlation
• Some static prediction schemes “forget” history on
returning from a procedure call
Fun_call:
if (x>0) return(1);
else return(0);
Main:
a = fun_call();
if (a>0) goto…
Direction taken by the branch inside the
procedure completely specifies the direction
taken by the branch outside
Branch Prediction Using Data Values
• Traditional approach
– Local history information
– Global path and history information
– Reducing table interference (better indexing)
• Use PCs and branch outcomes as input
– Do not contain all the information
Misprediction!!
How can one improve prediction of such branches?
Use Data Values!!!!!!!!!!!!
How to Use Data Values
• Speculative branch execution
• Using data values directly
• Second scheme was
chosen
• Lower-latency
prediction
• Some branches
can use combined
predictions
• Solution avoids
data value
prediction
Design Problems… (1)
• Large number of data values to store…
– Soln – store their difference
– Branch Difference Predictor (BDP)
• Backing Predictor
predicts most cases
• REP predicts the small
remaining subset
• Fringe cases with
exceptional
outcomes
• VHT provides ‘tagcheck’.
Design Problems… (2)
• Delay in updating data values
– Out-of-order execution, pipeline latencies
• BDC -- most recently committed branch
difference (indexed by branch PC)
• BCT -- count of the outstanding instances
• Staleness of the data value
• Operation
• Branch Fetch : Increment counter
• Branch Commit : Reset counter and
update difference cache
• REP replacement policy
• Replace least successful entries
• Randomly ignore a chunk of mispredictions
Search for a good predictor
design
Paper:
“A Language for Describing Predictors and its
Application to Automatic Synthesis”
Designing a predictor
• Components of a predictor
– Counters and History
– More?
• Formulate as a search problem
– How?
Search the design space
• Representation
– Feedback Loop Model + Primitives
Feedback/
Update (U)
Input
Index (I)
• P[w,d](I;U)
d
Prediction (P)
w
=>
parse tree
Examples
• Onebit[d](PC;T)
=P[1,d](PC; T)
• Counter[n,d](I;T)
= P[n,d](I; if T then P+1 else P-1)
• Twobit[d](PC;T)
=MSB(Counter[2,d](PC;T))
Genetic Programming
• Process
– Population, Evaluation, Selection and populate
next generation
• Operations on parse trees
– Replication, Crossover, Mutation,
Encapsulation
Discussion
• Comparable good prediction accuracy
• Complex but new components
• Rediscover counters and local/global
histories
• Predictors for other purposes, e.g. indirect
jump target prediction
End
• Comments?
• Thank you.