ACSAC’04

Choice Predictor for Choice Predictor for FreeFree

Mongkol EkpanyapongMongkol EkpanyapongPinar KorkmazPinar Korkmaz

HsienHsien--Hsin SHsin S. . LeeLee

School of Electrical and Computer EngineeringSchool of Electrical and Computer EngineeringGeorgia Institute of TechnologyGeorgia Institute of Technology

Atlanta, GA 30332Atlanta, GA 30332

Presented byDr. Youfeng WuDr. Youfeng WuIntel CorportaionIntel Corportaion

22Choice Predictor for Free

Branch Prediction SchemesBranch Prediction Schemes

Static Branch PredictionStatic Branch Prediction Predict at compile timePredict at compile time Based on inst. type or Based on inst. type or

profilingsprofilings Work well on easy-to Work well on easy-to

predict branch such as predict branch such as for-loopfor-loop

Dynamic Branch PredictionDynamic Branch Prediction Predict at run timePredict at run time Using hardware to track Using hardware to track

run-time behaviorrun-time behavior More accurateMore accurate Require more die area Require more die area

and power dissipationand power dissipation

33Choice Predictor for Free

Bimodal Branch PredictorBimodal Branch Predictor

Using counters to determine branch Using counters to determine branch directiondirection

Example of bimodal branch predictor Example of bimodal branch predictor

Branch addr

2 bit-counter

Counter > 1 :: hitCounter <= 1 :: miss

44Choice Predictor for Free

Global Branch PredictorGlobal Branch Predictor

Using global branch history to determine Using global branch history to determine the directionthe direction

Example of global branch predictorExample of global branch predictor

1 1 . . . . .

1 0

00…..0000…..0100…..10

11…..1111…..10

index

Branch History Pattern

Pattern History Table (PHT)Pattern History Table (PHT)

Rc-k Rc-1

Branch History Register (BHR)Branch History Register (BHR)(Shift left when update)

2 bit-counter

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Hybrid Branch PredictorHybrid Branch Predictor

Combining advantages of different branch Combining advantages of different branch predictorspredictors

Increasing branch prediction accuracyIncreasing branch prediction accuracyUse choice predictor to decide which Use choice predictor to decide which

branch predictors to favorbranch predictors to favorEmploys by many high performance Employs by many high performance

processorsprocessors

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Example of Hybrid Branch Example of Hybrid Branch PredictorPredictor

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MotivationMotivation

for i = 0 to 100000for i = 0 to 100000

a: if(i>0) …..a: if(i>0) …..

b: if(i%2 == 0) …..b: if(i%2 == 0) …..

end forend for

Choice Predictor

Branch address

Bimodal Predictor Global Predictor

Branch address Branch history

2 bit-counter

a: bimodalb: global

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OutlineOutline

IntroductionIntroductionRelated WorkRelated WorkStatic Prediction GenerationStatic Prediction GenerationExperimental ResultsExperimental ResultsConclusionConclusion

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Related WorkRelated Work

Profile guided branch prediction by Fisher and Profile guided branch prediction by Fisher and Freundenberger [ASPLOS,1992]Freundenberger [ASPLOS,1992]

Program based branch prediction by Ball and Larus Program based branch prediction by Ball and Larus [PLDI,1993][PLDI,1993]

Static correlated branch prediction by Yang and Smith Static correlated branch prediction by Yang and Smith [TOPLAS, 1999][TOPLAS, 1999]

Branch classification by Chang et al. [IJPP, 1999]Branch classification by Chang et al. [IJPP, 1999] Static methods in hybrid branch prediction by GrunwaldStatic methods in hybrid branch prediction by Grunwald Branch prediction on demand by Chaver [ISLPED, 2003]Branch prediction on demand by Chaver [ISLPED, 2003]

1010Choice Predictor for Free

OutlineOutline

IntroductionIntroductionRelated WorkRelated WorkStatic Prediction GenerationStatic Prediction GenerationExperimental ResultsExperimental ResultsConclusionConclusion

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Static Prediction Static Prediction GenerationGeneration

Profile guided static choice predictionProfile guided static choice predictionWe can eliminate choice predictorWe can eliminate choice predictor

Less areaLess areaLess power consumption for updatingLess power consumption for updating

We propose two classification modelsWe propose two classification modelsStatic Correlation Choice (SCC) prediction Static Correlation Choice (SCC) prediction Static Choice (SC) predictionStatic Choice (SC) prediction

1212Choice Predictor for Free

Static Choice PredictionStatic Choice Prediction

1313Choice Predictor for Free

SCC ModelSCC Model

If biased branch (more than 90%If biased branch (more than 90% hit rate) then favor bimodalhit rate) then favor bimodalCCLPLP == # consecutive taken and not taken for a given branch address# consecutive taken and not taken for a given branch addressE.g. branch pattern = 111100000101010 then CE.g. branch pattern = 111100000101010 then CLPLP = 3 + 4 = 7= 3 + 4 = 7

CCGPGP == # consecutive taken and not taken of k-history group for a given # consecutive taken and not taken of k-history group for a given branch addressbranch address

E.g. Suppose k =2 and branch history is as follows:E.g. Suppose k =2 and branch history is as follows:101000001111 for the 00 group, 11111111110 for the 01 group, 101000001111 for the 00 group, 11111111110 for the 01 group, 1110 for the 10 group, and 1000000 for the 11 group1110 for the 10 group, and 1000000 for the 11 groupThen CThen CGP GP = 7 + 9 +2 +5 = 22.= 7 + 9 +2 +5 = 22.

If CIf CLPLP >> CCGPGP then favor bimodal branch predictorthen favor bimodal branch predictorElse favor global branch predictorElse favor global branch predictor

1414Choice Predictor for Free

SC ModelSC Model

Using results collected from software Using results collected from software based choice predictor from simulatorbased choice predictor from simulator

Count number of predication using Count number of predication using bimodal and global branch prediction, bimodal and global branch prediction, favor the majorityfavor the majority

This can be done by incorporate simulator This can be done by incorporate simulator into profile guided compilerinto profile guided compiler

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Comparing between SCC and Comparing between SCC and SCSC

SC model consider SC model consider aliasing into accountaliasing into account

Consider if branch a and Consider if branch a and c destructively interfere c destructively interfere each other in global each other in global branch predictor tablebranch predictor table

SCC will still favor global SCC will still favor global branch predictorbranch predictor

SC will favor bimodalSC will favor bimodal

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OutlineOutline

IntroductionIntroductionRelated WorkRelated WorkStatic Prediction GenerationStatic Prediction GenerationExperimental ResultsExperimental ResultsConclusionConclusion

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Simulation InfrastructureSimulation Infrastructure Execution Engine Out-of-order

Fetch Width 8 instruction

Issue Width 8 instruction

ALU Units 4 units

Branch Target Buffer 4-way, 4096 sets

Register Update Unit 128 entries

Cache organization 4-way split I- and D-L1:

64 KB each

2 cycle hit latency

32 bytes line

4-way L2(unified):

512 KB

16 cycle hit latency

64 bytes line

Memory latency 120 core cycles

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Miss Prediction RatesMiss Prediction Rates

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

gzip vpr mcf crafty parser perl gap bzip2 twolf Avg

hybrid_g10 hybrid_g10+scc hybrid_g10+sc hybrid_g10+rand

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Normalized SpeedupNormalized Speedup

0.95

1.05

1.15

1.25

1.35

gzip vpr mcf crafty parser perl gap bzip2 twolf Avg

hybrid_g10 hybrid_g10+scc hybrid_g10+sc hybrid_g10+rand

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Normalized Power Normalized Power ConsumptionConsumption

0

0.5

1

1.5

2

2.5

3

3.5

gzip vpr mcf crafty parser perl gap bzip2 twolf Avg

hybrid_g10 hybrid_g10+scc hybrid_g10+sc hybrid_g10+rand

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Normalized Processor Normalized Processor EnergyEnergy

0.85

0.87

0.89

0.91

0.93

0.95

0.97

0.99

1.01

gzip vpr mcf crafty parser perl gap bzip2 twolf Avg

hybrid_g10 hybrid_g10+scc hybrid_g10+sc hybrid_g10+rand

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Normalized Performance on Normalized Performance on Different Input SetsDifferent Input Sets

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Normalized Performance on Normalized Performance on Different Hybrid Branch Different Hybrid Branch

PredictorsPredictors

0.95

1

1.05

1.1

1.15

1.2

1.25

1.3

1.35

gzip vpr mcf crafty parser perl gap bzip2 twolf Avg

hybrid_g10 hybrid_g10_sc hybrid_PAg hybrid_PAg_sc hybrid_GAp hybrid_GAp_sc

2424Choice Predictor for Free

Normalized Power on Different Normalized Power on Different Hybrid Branch Predictors Hybrid Branch Predictors

0.95

1.05

1.15

1.25

1.35

gzip vpr mcf crafty parser perl gap bzip2 twolf Avg

hybrid_g10 hybrid_g10_sc hybrid_PAg hybrid_PAg_sc hybrid_GAp hybrid_GAp_sc

2525Choice Predictor for Free

Normalized Energy on Normalized Energy on Different Hybrid Branch Different Hybrid Branch

PredictorsPredictors

0.60.650.70.750.80.850.90.95

11.05

gzip vpr mcf crafty parser perl gap bzip2 twolf Avg

hybrid_g10 hybrid_g10_sc hybrid_PAg hybrid_PAg_sc hybrid_GAp hybrid_Gap_sc

2626Choice Predictor for Free

Normalized Area of Normalized Area of Hybrid branch predictorsHybrid branch predictors

Branch predictor Normalized area

gshare11 1.9822

gshare12 4.806

hybrid_g10 2.973

hybrid_g10+scc/sc 1.986

hybrid_g11 3.955

hybrid_g11+scc/sc 2.968

hybrid_PAg 4.946

hybrid_PAg+sc 3.959

hybrid_GAp 3.713

hybrid_Gap+sc/scc 2.726

2727Choice Predictor for Free

OutlineOutline

IntroductionIntroductionRelated WorkRelated WorkStatic Prediction GenerationStatic Prediction GenerationExperimental ResultsExperimental ResultsConclusionConclusion

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ConclusionConclusion

We propose two profile-guided techniques We propose two profile-guided techniques Static Correlation Choice and Static Static Correlation Choice and Static ChoiceChoice

From this, we can eliminate the need of From this, we can eliminate the need of choice predictor which can save power choice predictor which can save power (40% on avg.) and area (27% on avg.)(40% on avg.) and area (27% on avg.)

It can be extended to more complex It can be extended to more complex branch predictors with more complex branch predictors with more complex choice predictorchoice predictor

2929Choice Predictor for Free

Thank YouThank You