andré seznec caps team irisa/inria 1 analysis of the o-gehl branch predictor optimized geometric...

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André Seznec Caps Team

IRISA/INRIA

Analysis of the O-GEHL branch predictor

Optimized GEometric History Length

André Seznec IRISA/INRIA/HIPEAC

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André SeznecCaps Team

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Objectives

State of the art accuracy:

Any gain in branch prediction accuracy results in performance gain and power consumption gain

Keep the design implementable: Rely on a single prediction scheme Use only global history information:

Designers hate maintaining speculative local history

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L(0) ?

L(4)

L(3)

L(2)L(1)

TOT1

T2

T3

T4

The basis : A Multiple history length predictor

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Selecting between multiple predictions

Classic solution: Use of a meta predictor

“wasting” storage !?!

chosing among 5 or 10 predictions ??

Neural inspired predictors: Use an adder tree instead of a meta-predictor

Let’s use the adder tree

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L(0) ∑

L(4)

L(3)

L(2)L(1)

TOT1

T2

T3

T4

Final computation through a sum

Prediction=Sign

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From “old experience” on 2bcgskew

Some applications benefit from 100+ bits histories

Some don’t !!

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GEometric History Length predictor

L(1)1iαL(i)

0 L(0)

The set of history lengths forms a geometric series

{0, 2, 4, 8, 16, 32, 64, 128}

What is important: L(i)-L(i-1) is drastically increasing

Spends most of the storage for short history !!

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Update policy

Perceptron inspired threshold based

Perceptron-like threshold did not work

Reasonable fixed threshold= Number of tables

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Dynamic update threshold fitting

On an O-GEHL predictor, best threshold depends on the application the predictor size the counter width

By chance, on most applications, for the best fixed threshold,

updates on mispredictions ≈ updates on correct predictions

Monitor the difference

and adapt the update threshold

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Adaptative history length fitting (inspired by Juan et al 98)

(½ applications: L(7) < 50) ≠

(½ applications: L(7) > 150)

Let us adapt some history lengths to the behavior of each application

8 tables: T2: L(2) and L(8) T4: L(4) and L(9) T6: L(6) and L(10)

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Adaptative history length fitting (2)

Intuition: if high degree of conflicts on T7, stick with

short history

Implementation: monitoring of aliasing on updates on T7

through a tag bit and a counter

Simple is sufficient: Flipping from short to long histories and

vice-versa

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Evaluation framework

1st Championship Branch Prediction traces:

20 traces including system activity

Floating point apps : loop dominated

Integer apps: usual SPECINT

Multimedia apps

Server workload apps: very large footprint

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Reference configurationpresented for Championship Branch Prediction

8 tables: 2 Kentries except T1, 1Kentries 5 bit counters for T0 and T1, 4 bit counters

otherwise 1 Kbits of one bit tags associated with T7

10K + 5K + 6x8K + 1K = 64K

L(1) =3 and L(10)= 200 {0,3,5,8,12,19,31,49,75,125,200}

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A case for the OGEHL predictor

2nd at CBP: 2.82 misp/KI

Best practice award: The predictor the closest to a possible hardware

implementation Does not use exotic features:

• Various prime numbers, etc• Strange initial state

Very short warming intervals: Chaining all simulations: 2.84 misp/KI

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A case for the OGEHL predictor (2)

High accuracy 32Kbits (3,150): 3.41 misp/KI

better than any implementable 128 Kbits predictor before CBP

• 128 Kbits 2bcgkew (6,6,24,48): 3.55 misp/KI• 176 Kbits PBNP (43) : 3.67 misp/KI

1Mbits (5,300): 2.27 misp/KI• 1Mbit 2bcgskew (9,9,36,72): 3.19 misp/KI• 1888 Kbits PBNP (58): 3.23 misp/KI

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A case for the OGEHL predictor (3)

Robustness to variations of history lengths choices: L(1) in [2,6], L(10) in [125,300] misp. rate < 2.96 misp/KI

Geometric series: not a bad formula !! best geometric L(1)=3, L(10)=223, 2.80 misp/KI best overall {0, 2, 4, 9, 12, 18, 31, 54, 114, 145,

266} 2.78 misp/KI

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Impact of the number of components

4 components — 8 components• 64 Kbits: 3.02 -- 2.84 misp/KI• 256Kbits: 2.59 -- 2.44 misp/KI• 1Mbit: 2.40 -- 2.27 misp/KI

6 components — 12 components• 48 Kbits: 3.02 – 3.03 misp/KI• 768Kbits: 2.35 – 2.25 misp/KI

4 to 12 components bring high accuracy

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Impact of counter width

Robustness to counter width variations: 3-bit counter, 49 Kbits: 3.09 misp/KI

Dynamic update threshold fitting helps a lot

5-bit counter 79 Kbits: 2.79 misp/KI

4-bit is the best tradeoff

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Prediction computation time

3 successive steps: Index computation: a 3-entry XOR gate Table read Adder tree

May not fit on a single cycle: But can be ahead pipelined !

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Ahead pipelining a global history branch predictor (principle)

Initiate branch prediction X+1 cycles in advance to provide the prediction in time Use information available:

• X-block ahead instruction address• X-block ahead history

To ensure accuracy: Use intermediate path information

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Practice

Ahead OGEHL:8 // prediction computations

bcd

Ha

A

A B C D

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Ahead Pipelined 64 Kbits OGEHL

3-block: 2.94 misp/KI 4-block: 2.99 misp/KI 5-block: 3.04 misp/KI

Not such a huge accuracy loss

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A final case for the O-GEHL predictor

delivers state-of-the-art accuracy uses only global information:

Very long history: 200+ bits !! can be ahead pipelined many effective design points

Nb of tables, counter width, history lengths prediction computation logic complexity is low

(compared with concurrent predictors)

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The End

andré seznec caps team irisa/inria 1 analysis of the o-gehl branch predictor optimized geometric...

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