Dynamic Voltage SchedulingUsing

Adaptive Filtering of Workload Traces

Amit Sinha and Anantha Chandrakasan

Massachusetts Institute of Technology

Sinha, VLSI ’01 2

Overview

n Introductionn Typical Workload Profilen DVS Basics

n Energy Workload Modelsn Workload Prediction

n Markov Processesn Various Algorithms

n Energy Performance Tradeoffsn Results and Conclusions

Sinha, VLSI ’01 3

Typical Processor Workload Profiles

Pro

cess

or

Uti

liza

tio

n (

%)

Time (s)

Dialup Server

WorkstationFileserver

Sinha, VLSI ’01 4

Dynamic Voltage Scaling

ACTIVE IDLE

EFIXED = ½ C VDD2

Fixed Power Supply

ACTIVE

EVARIABLE = ½ C (VDD/2)2 = EFIXED / 4

Variable Power Supply

0.2 0.4 0.8 1.0

0.2

0.4

0.6

0.8

1.0

Normalized Workload

Nor

mal

ized

Ene

rgy

Fixed Supply

VariableSupply

00 0.6

Sinha, VLSI ’01 5

Enabling Technology

n Variable frequency processors availablen Transmeta’s Crusoe

n LongRun Technology

n AMD K6-2+n PowerNOW!

n Mobile Pentium IIIn SpeedStep

StrongARM

n StrongARM SA-1100n 59MHz – 206MHz (0.8V – 1.5V) DVS Circuit

Sinha, VLSI ’01 6

Energy Workload Model

Workload (r)

Rel

ativ

e C

urr

ent

(I/

I ma

x)

Relative Current Load (I/Imax)

Rel

ativ

e E

ffic

ien

cy (

%)

( )2

2

00

20 22

+++=

rVV

rr

VV

rfTCVrE ttrefs

[Gutnik97]

( )

+++=

2

00

0

22r

VV

rrVV

VV

rIrI tt

refref

Workload (r)

No

rmal

ized

En

erg

y

No Voltage Scaling

DVS with Converter Efficiency

Ideal DVS

Energy vs. WorkloadDC/DC Efficiency

Current vs.Workload

Sinha, VLSI ’01 7

Workload Prediction

n How to predict workload, w?n How frequently processing rate, f(r), be updated

Variable VoltageProcessor

DC

/DC

C

on

vert

er

Wo

rklo

ad

Mo

nit

or

Vfixed

V(r) w f(r)

r

?1

?2

?n

Task Queue

?

Can be modelled asa Markov Process

Sinha, VLSI ’01 8

Prediction Algorithms

Least Mean Square (LMS)

Expected Workload State (EWS)

Exp. Weighted Average (EWA)

Moving Average Workload (MAW)

knN

khn ,1

][ ∀= kn akh −=][

{ } ∑=

=+Ε=+L

jijj pwnwnw

0

]1[]1[ ][][][][1 knwnwkhkh enn −+=+ µ

• Simplest• Peformance degradation with fast loads

• Lower significance of older data• Event predictition context [Hwang97]

• Adaptive filter, self-adjusting• Convergence issues

• Probabilistic fomulation• Transition matrix updated every slot

∑−

=

−=+1

0

][][]1[N

knp knwkhnwPredicted

WorkloadPrevious

Workloads

Sinha, VLSI ’01 9

Prediction Performance

n Best prediction with LMS and about 3 taps

RM

S E

rro

r

Filter Taps (N)

MAW

EWS

LMS

EWA

n Averaged over different processors and times

n 1 sec update raten 1 hour processor

utilization snapshots

Less TapsNoisy Prediction

More TapsExcessive LPF

Sinha, VLSI ’01 10

LMS Tracking of Workload

Time (s)

Wo

rklo

ad

Continuous

Prefect

Predicted

N = 3T = 10Levels = 10µ = 0.1

Sinha, VLSI ’01 11

Energy Performance Tradeoff

n Averaging is energy efficient

T 2T

Time

Wor

kloa

d 1.0

0.5

W1W2

0.675

Ener

gy

1.0

0.5

W1 W2

0.5625

)()(22

221

22

21 rErE

rrrr≥→

+

≥+

DecreasedAveraging

Higher EnergyFaster Response

Increased Averaging

Lower EnergySluggish

Performance

n Update time T depends onn Maximum allowed performance hitn DC/DC converter and frequency change overheads

Sinha, VLSI ’01 12

Update Time (s)

Per

form

an

ce H

it

F max

F avg

N = 2

N = 6

N = 10

Maximum allowed performance hit

Tmax

Performance Hit Metric

n Performance Hit Function

t

tt

rrw

t∆

∆∆ −=∆ )(φ

Maximum can be used set update time

n Maximum and Average

)(),(max tt Tavg

T ∆∆ φφ

Sinha, VLSI ’01 13

No

rma

lize

d E

ner

gy

Update Time, T (s) Filter Taps (N

)

Optimum Update Time and Taps

n N, T selections are not completely independent!

N = 3T = 5 s

n Good choice

Sinha, VLSI ’01 14

Discrete Processing Levels

n Discrete frequency levels are not too bad.n StrongARM has 11 levels [ degradation < 5% ]

Eac

tual

/ E p

erfe

ct

Processing Levels (L)

N = 3T = 5LMS Filter

Sinha, VLSI ’01 15

Results

36.310.81.112.1EWS

35.410.61.092.2EWA

43.114.71.032.3LMS

42.812.61.41

3.3

16.7

23.576.7

MAW

FileServer

33.87.41.5015.7EWS

37.49.21.4116.7EWA

47.714.11.2019.6LMS

35.33.65.22

1.6

52.7

275.2445.9

MAWUserWork-Station

35.13.84.6359.5EWS

35.63.75.2852.1EWA

36.03.95.1953.0LMS

2.2

Actual

1.2

Max / Perfect

ESR Comparison

1.10

Perfect / Actual

10.6

F avg

(%)

34.8

2.42.9

MAW

DialupServer

PerfectMaxF max

(%)

Energy Savings Ratio (ESR)FilterTrace

Sinha, VLSI ’01 16

Conclusions

n DVS is very effective for energy reductionn Upto 2 orders of magnitude savings possiblen About 30% ‘instantaneous’ performance loss

n Averaged workloads are bestn Makes system sluggish to workload changesn Unknown a priori

n Energy Performance Tradeoffn Faster updates lower visible performance lossn Faster updates also mean increased energy

n Workload prediction is crucialn Adaptive LMS filtering is quite effective