energy-proportional computing: a new definition - david wood

7/25/2019 Energy-Proportional Computing: A New Definition - David Wood

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Energy

-

Proportional Computing:

A New DefinitionDavid A. Wood

Professor, Computer Sciences

2/4/16 UNIVERSITY OF WISCONSIN-MADISON 1


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Outline

! Background & Motivation

! New ideals

! New metrics

! Management

! Summary



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Datacenter Energy Consumption

! U.S. data center energy [NRDC, Anthesis]

! 2013: 91B KWh ! 34 coal-fired 500-MW power plants, 1yr

! 2020: 140B KWh ! 50 coal-fired 500-MW power plants , 1yr

!

! $13B/yr electricity costs


Typical datacenterenergy consumption

breakdown[Dimension Data]


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PDUsSwitchgearsGeneratorsUPS

Power

ChillersPumpsCRAHsCRACsDX

Cooling

Lighting

Misc

Building Load

Servers

StorageNetworksMonitorsWorkstationsLaptops

IT LoadTotal

FacilityEnergy

IT EquipmentEnergy

PUE: Power Usage Effectiveness

! PUE =!"#$% '$()%)#* +,-./*0! +12)34-,# Energy

!1


[The Green Grid]


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Datacenter Energy Consumption

! U.S. data center energy [NRDC, Anthesis]

! 2013: 91B KWh ! 34 coal-fired 500-MW power plants, 1yr

! 2020: 140B KWh ! 50 coal-fired 500-MW power plants , 1yr

!

! $13B/yr electricity costs


Typical datacenterenergy consumptionbreakdown[Dimension Data]


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! PUE =!"#$% '$()%)#* +,-./*0! +12)34-,# Energy

!1


[The Green Grid]


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! PUE > 1 " energy waste due to non-IT infrastructure

! Strong impact in reducing non-IT energy waste

! PUE estimates for current datacenters! Allied Control: 1.02, Facebook: 1.08, Google: 1.08, avg. 1.12,

Green IT Cube: 1.07, Microsoft: 1.125, OVH: 1.09

! Average: 1.7

! Missing: IT equipment energy waste

! This talk focuses on server energy



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Energy Efficiency

! # =5".6

Energy=

7-.8".4$,(-Power

!Performance = Load served

! Examples: BIPS/Watt, ssj_ops/Watt, Instructions/nJ,

! #max $Emin



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EP: Energy Proportionality

! # =5".6

Energy=

7-.8".4$,(-Power

!EP: Energy%Work [eliminates energy waste ]! equiv., Power % Performance

! equiv., constant #

! First proposed by Barroso and Hlzle, 2007

! Does EP imply Emin? Converse? Identical goals?



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Conventional Wisdom

! We see that peak energy efficiency occurs atpeak utilization and drops quickly as utilizationdecreases. Luiz Andr Barroso and Urs Hlzle,IEEEComputer, 2007

! Energy waste at low utilizations/loads! Servers are typically 10 50% loaded

! EP as a primary design goal!

Strong impact! Low idle power, wide dynamic power range

! Later, Dynamic EP [Lo et al., ISCA 2014]! Accounts for unavoidable idle power



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Example: SpecPower on Haswell

! Java-based load-varying workload

! 3 calibration intervals to detect max. load (100%)

! Measurement interval Loads: 100%, 90%, 80%, , 10%, Idle

!Performance: transactions per second

! Power: system power

! Quad-core (eight-thread) Intel Haswell server



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EP on Conventional Server



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Results from 2014


[Lo, et al. ISCA 2014]


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Efficiency of Conventional Server



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Efficiency of Conventional Server



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(more) Conventional Wisdom

! In an energy-proportional system, explicit power

management is unnecessary, as power consumptionvaries naturally with utilization. Meisner, Gold, Wenisch,PowerNap: eliminating server idle power,ASPLOS 2009

! Ideally systems would exhibit energy-

proportionality, wherein servers consume power in

proportion to their load. Meisner, Sadler, Barroso, Weber,Wenisch,Power management of online data-intensive services, ISCA 2011

! ...; modern servers are only maximally efficient at

100%. Meisner, Wenisch,Does low-power design imply energyefficiency for data centers?, ISLPED 2011



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Current (Reconfigurable) Systems

! Reconfigurable resources

! DVFS

! Number of cores

!

Threads per core! Prefetching

! Cache size

!

! Multiple configurations may serve the same load

! Different power & energy consumptions



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EP on Reconfigurable Systems



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Pareto Frontier

! Pareto efficiency/optimality

! Subset of (performance, power) tuples in state space

! Cannot increase performance without also using more power! Cannot reduce power without also decreasing performance

! States that achieve goals will lie on the frontier

! Minimum E, ED, ED2

, ! Minimum power with/without a performance target

! Maximum performance with/without a power cap

!


Vilfredo

Pareto18481923[Wikipedia]


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Reconfigurable System Efficiency



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Limitations of conventional views

! Systems may achieve better than ideal efficiency

! Better than the best!!

! Neither EP nor Dynamic EP is ideal

! Conventional models based on fixed-resourceassumptions

! Modern systems are highly reconfigurable

! New need ideals and new metrics

! Quantify energy waste w.r.t ideal

! Quantify factors that contribute to waste



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Outline


! New ideals

! New metrics

! Management

! Summary



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Conventional Ideals

! Design Ideal! For system designers

! Help design energy efficient systems

! Examples: EP

! Helped drive down idle power in current systems

! Operational Ideal! Characterizes maximum operating efficiency

!

Help system operators and OS schedulers?! Example: Dynamic EP

! Quantifies divergence from ideal linear behavior

! Need new ideals for reconfigurable systems



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New Ideals

! Design Ideal

! EOP Energy Optimal Proportional

! Determined by #max

! Current systems energy optimal configuration

! Best we can do irrespective of of offered load

! Operational Ideal

! Dynamic EO Dyanamic Energy Optimal

! Determined by the Pareto frontier

! Realizable ideal for the current system

! Best we can do for the offered load



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Conventional vs New Ideals



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New Design Ideal



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New Operational Ideal



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Conventional vs New Ideals


EP EOP

Dynamic EP Dynamic EO

Design

Operational

Conventional New

Dynamic EP " EP " EOP

Dynamic EO " EOP

non-Sub-Linear" Dynamic EP " Dynamic EO


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Optimality vs Proportionality

! Optimality at every load Proportionality

! EOP is always proportional

! Uses Emin at every load

!

Proportionality Optimality! EP may not be optimal

! Uses !Emin energy at every load


Emin = Energy needed by EOP


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Goal Dependence

! Minimize E

! EOP uses power linearlyproportional to load

! Constant # regardless of load

! Minimize ED

! EDOP uses power quadratically proportional to load

! Minimize ED2

! ED2OP uses power cubicallyproportional to load

!



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Outline


! New ideals

! New metrics

! Management

! Summary



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PDUsSwitchgears

GeneratorsUPS

Power

ChillersPumpsCRAHsCRACsDX

Cooling

Lighting

Misc

Building Load

Servers

StorageNetworksMonitorsWorkstationsLaptops

IT LoadTotalFacility

Energy

IT EquipmentEnergy


! PUE =!"#$% '$()%)#* +,-./*0! +12)34-,# Energy

!1


[The Green Grid]

?

CPUE


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! PUE =!"#$% '$()%)#* +,-./*0! +12)34-,# Energy

!1


[The Green Grid]

?CPUE


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CPUE: Computational PUE

! Goal: To quantify waste in computational energy! Inspired by PUE

! Informally:

! Depends upon configuration c and load l> 0

! E(c,l) = CPUE(c,l)# Emin

! CPUE(c,l) > 1wastes energy


CPUE(c,l) =Actual server energy with catl

EOP server energy =

E(c,l)Emin

!1

CPUE =

Actual IT server energy

EOP server energy !

1


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Decomposing CPUE

! For configuration c and load l> 0


CPUE(c,l) =Actual server energy with catl

EOP energy

=E(c,l)Emin

=&:max&:;c,l?@A

BCDECF@G

HIJKCLMK?DIJ

BCDECF@ N

OPM=AJ

HIJKCLMK?DIN

>?@A

OPM=A

! Factor performance to focus designers attention

! I/P: Instruction set architectures, compilers, algorithms

! CPI: Pipelines, caches, predictors, out-of-order execution

! T/C: Technology, circuits, pipelines



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Iron Law of Energy Efficiency

! Iron Law of Energy Efficiency:

E(c,l) = LUE(l)# RUE(c,l)# Emin

! Factor energy efficiency to focus designers attention

! LUE: Load Usage Effectiveness

! Operate server at efficient load level

! RUE: Resource Usage Effectiveness

! Configure server to run efficiently at a given load level

! Emin: Optimal energy for a given computation on this server



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LUE: Load Usage Effectiveness



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LUE: Load Usage Effectiveness



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RUE: Resource Usage Effectiveness



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RUE: Resource Usage Effectiveness



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Energy Waste: Root Cause Analysis

! Non-optimal loads

! LUE(l) > 1" energy waste

! There exists another load that uses less energy

! LUE(l) = 1$ system operating with Q&max! Only at points where Dynamic EO and EOP lines meet

! Non-optimal configurations

! RUE(c,l) > 1" energy waste

! There exists another configuration that uses less energy but canserve the same load

! RUE(c,l) = 1$ c is at Dynamic EO



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Outline


! New ideals

! New metrics

! Management

! Summary



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Load Management

! Goal: Choose lsuch that LUE(l) is reduced

! Select subset of Dynamic EO close to EOP

!Global: Inter-server! Load distribution among servers

! Difficult for stateful, time-sensitive services



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Load Management

! Optimization problem

! Assume n servers, ith servers Dynamic EO is described by set

{(xi,R, yi,R), , (xi, j, yi, j), }

! LetLbe the total load to be served

Minimize Pwr = S S H? TU#P?TUVWXYZ

Such that L ' S S H?TU #[?TUVWXYZ

Ii, j( {0,1} for all i,jS H?TUV ' 1 for all i

! Load for server iis S H? TU#[? TUV



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Configuration Management

! Goal: Choose c such that RUE(c,l) is reduced

! Operate system at/near Dynamic EO

! Local: Intra-server

! Existing Linux governors not adequate

! Limited knobs (DVFS)

! Ondemand governor ) peak performing configuration

! Powersave governor (lowest frequency configuration)

! Limits performance

! Operates in high LUE region



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Reactive Governor

! Knobs: core frequency and cache prefetch enable

! R(t): parametrized by length of interval = t ms

! Track Active Cycles for last interval

! Select highest frequency such that all cycles are expected to be

active

! Exponential Ramp-up

! Inspired by Ethernet backoff

! If selected frequency !current frequency

! Increase selected frequency by step

! Double step

! Prefetch control

! Profile with OFF and ON for (10 + 10) ms within every interval



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Reactive Governor


T=s T=s+10 T=s+20 T=s+t-20

PF PF

perf1 perf2

f = current frequency

perf1 >perf2?

PF

Y

N #Active Cyclesin last t ms?

Calculate target

freq (tfr)

tfr!f?

YN

Init steptfr+=stepstep*=2

f"tfr


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Reactive Governor



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Reactive Governor: RUE



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Reactive Governor



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Reactive Governor: RUE



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SLA-Aware Governors

! Disconnect between user needs and OS governors

! SLAs! Maximize energy efficiency

! Subject to max. response time

! Maximize performance within a power cap! RAPL mechanism only seems to deal with DVFS

! Other knobs?

! Minimize power for a performance target

!

! Intel Skylake Speed Shift! Hardware P states (DVFS)

! Other knobs?

! OS specifies performance hints



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Challenges

! Available knobs?

! Hardware has many knobs

! But few exposed to operating system

! Efficient construction of Pareto frontier (Dynamic EO)

! Transition overhead! Speed Shift: )1 msec

! Caches? Interconnects?

! Measurement interval! RAPL: few (>1) ms

! Wall power: 0.1 1 sec



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RAPL Power # System Power

! Advantage: shorter reaction times



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Summary

! Conventional ideal models no longer adequate

! EP does not imply Emin

!

New ideals! EOP as a primary design goal

! Dynamic EO as a primary operational goal

!

New metrics: CPUE, LUE, RUE! Quantify & attribute server energy waste

! New SLA-aware governors needed



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Acknowledgments

! Funding

! NSF grant CCF-1218323

! NSF grant CNS-1302260

! Financial interest in AMD, Google


Rathijit SenPh.D. Candidate


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Backup



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References

! NRDC, Anthesis: http://www.nrdc.org/energy/files/data-

center-efficiency-assessment-IP.pdf

! Dimension Data:

https://www.dimensiondata.com/Global/Downloadable%20Documents/The%20Relationship%20Between%20Data%20Centre%20Strategy%20and%20Energy%20Efficiency%20Whitepa

per.pdf

! The Green Grid:

http://www.thegreengrid.org/~/media/WhitePapers/WP49-PUE%20A%20Comprehensive%20Examination%20of%20the

%20Metric_v6.pdf



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References

! PUE numbers! Allied Control: http://multimedia.3m.com/mws/media/1127920O/2-

phase-immersion-coolinga-revolution-in-data-center-efficiency.pdfFacebook: https://www.facebook.com/PrinevilleDataCenter/app/399244020173259/Google: http://www.google.com/about/datacenters/efficiency/intern

al/index.html#measuring-efficiencyGreen IT Cube: http://www.heise.de/newsticker/meldung/Green-IT-Cube-Hocheffizientes-Supercomputer-Domizil-eingeweiht-3082605.htmlMicrosoft: http://download.microsoft.com/download/8/2/9/8297F7C7-AE81-4E99-B1DB-D65A01F7A8EF/Microsoft_Cloud_Infrastructure_Datacenter_and_

Network_Fact_Sheet.pdfOVH: https://www.ovh.com/ca/en/about-us/green-it.xmlAverage: http://www.datacenterknowledge.com/archives/2014/06/02/survey-industry-average-data-center-pue-stays-nearly-flat-four-years/