but, havent we done a lot? of course, power is important

1 October 2003 Copyright © 2006 HP corporate presentation. All rights reserved.

A S i f P M t

© 2008 Hewlett-Packard Development Company, L.P.The information contained herein is subject to change without notice

A Science of Power Management a systems perspectiveParthasarathy RanganathanDistinguished Technologist, Hewlett Packard Labs

What does a science of power pmanagement mean to me?

Prior body of workchips to datacentersmobile to enterprise

ApproachEnergy-aware user interfacesHeterogeneity-based architecturesPower-aware blade serversIntegrated IT/facilities resource mgmtProfit-aware schedulingUnified power management archMicroblades and megaserversPower-aware networkingT l t i b h k

pmetrology to solutions

Interesting topics for us to ponder…

Tools, metrics, benchmarks, … Joulesort, Zesti, WeathermanConsil, Splice, eprof, …

hpl.hp.com/personal/partha_ranganathan

Why do we need a science of power pmanagement?

Of course, power is important

Battery, electricity, environment, heat, compaction, reliability, …

But, havent we done a lot?[Ranganathan, EE282 class notes]


Yes!…but

Need much lower lower power

[Starner, IBM Systems Journal,1996]

Solar energy?

9 10 April 2009

energy?164W/sqm * 9% * 3cmx2cm * 50

deration for non-sunny periods = 10mW

Increased compaction…

not enough cooling?Customer Facility Examples

Datacenter average Watts per square footDatacenter average Watts per square foot

150

200

300 Genetic researchGenetic research

Palo AltoPalo Alto Web hosting service 2Web hosting service 2Web Hosting Service 1Web Hosting Service 1

HP Houston datacenterHP Houston datacenter 10kW-20kW/

Wat

ts/f

oot

Wat

ts/f

oot22

Size of Datacenter25

35

45

55

65

75

85

95

105 Nat. Lab 2Nat. Lab 2Nat. Lab 1Nat. Lab 1

Banking 1Banking 1

Financial ServicesFinancial Services

Bank4Bank4

Bank3Bank3

Bank2Bank2

Insurance 1Insurance 1

Corporate Corporate database sitedatabase siteTelecom siteTelecom site

Site1Site1Site2Site2

Site3Site3

Site4.1Site4.1

Site4.2Site4.2

Site6.1Site6.1

Site6.2Site6.2

Site7

Site4.2Site4.2

Site8.1Site8.1

Site8.2Site8.2

Site9

3.5kW-5kW/rack

$40 Billion


The cost of power and cooling is likely to exceed that of

h dhardware…Luiz Barroso, Google

More work needed on power mgmt…p g

So, how can a formal science help? p

Formulating th blthe problem

One billion pentiumsin one handheld!

Another quick experiment

! Laptop: 165X! Handheld: 15X! Cell phone: 6X! RIM pager: 92 mW

Radio wakeup:• 100ms (iPAQ); • 1.2 sec (cell) • 5 sec (RIM)

Data from [PACS2003]


!Can we identify minimum energy gyneeded for a task?

(optimality?)

minimum energy needed for a task…

How do we define energy & work done?

MIPS per Watt? Energy-Aware User Interfaces

MIPS per Watt?

! " dd PULKLK $12111 ###

Burdened costs of power

! "hardwareconsumedgrid PULKLK $,12111 ###

0.050 KW 1000+ KW10 - 15 KW

1000 KW1 KW0.005 KW

Heat Generated

Energy to Remove Heat

.250 KW

.025 KW


Supply vs demand side power efficiencies power efficiencies

Supply vs demand side power efficiencies

Energy in extraction, reclamation, transport, manufacturing, …

Energy in operation

MIPS per Watt?

"How do we measure and reason about energy efficiency?energy efficiency?

Thinking about th l tithe solution


ConsumptionProvision

(Compute nodes)

(Feeds, PDUs, UPS, etc.)

Extraction

(CRAC units, floor tiles, etc.)

Landscape of Power Optimizations

• Voltage scaling/islands• Clock gating/routingClock-tree distribution, half-swing clocks

• Redesigned latches/flip-flops

• Voltage/freq scaling• GatingPipeline, clock, functional units, branch prediction, data path • Split instrucn windows

• Switching controlRegister relabeling, operand swapping, instruction scheduling

• Memory access reduceLocality optimizations, register allocation

Average power, peak power, power density, energy-delay, …

CIRCUITS ARCHITECTURE COMPILER, OS, APP

pin-ordering, gate restructuring, topology restructuring, balanced delay paths, optimized bit transactions

• Redesigned memory cellsLow-power SRAM cells, reduced bit-line swing, multi-Vt, bit line/word line isolation/segmentation

• Other optimizationsTransistor resizing, GALS, low-power logic

• SMT thread throttling

• Bank partitioning• Cache redesignSequential, MRU, hash-rehash, column-associative, filter cache, sub-banking, divided word line, block buffers, multi-divided module, scratch

• Low-power states• DRAM refresh-control

• Switching controlGray, bus-invert, address-increment

• Code compression• Data packing/buffering

• Power-mode-control

• CPU/resource schedule• Memory/disk controlDisk spinning, page allocation, memory mapping, memory bank control

• NetworkingPower-aware routing, proximity-based routing, balancing hop count, …

• Distributed computingMobile agents placement, network-driven computation

• Fidelity control• Dynamic data types• Power API

Avoid waste!

How do we avoid waste?avoid waste?

A framework to ti i optimize power

[CACM09]

A taxonomy of inefficiencies(how do we identify waste?)

A taxonomy of techniques(how do we reduce waste?)

General-purpose designse.g., convergence, mission-critical

Design process structuree.g., modularity, aggregation

Bad design choices & business constraints

Design for futuree.g., unforeseen or worst-case

End-user experiencee.g., tethered system hangover

Inefficiencies


avoiding waste & improving power efficiency

!Energy-efficient technologiese.g., replace disk with flash, replace copper with optics, …

"Match power to worke.g., energy proportionality; turn-off/dial-down things, …

#Match work to power

38 10 April 2009

pe.g., asymmetric multicores, temp-aware scheduling, …

$Piggy back energy eventse.g., shared caches, coalesced request streams, …

%Special purpose solutionse.g., GPUs, ASICs, …

&Cross layers for efficiencye.g., ensemble power management, …

'Tradeoff some other metrice.g., fidelity-aware energy management

(Tradeoff the uncommon-case

39 10 April 2009

e.g., power-supply efficiency, …

)Spend somebody’s powere.g., remote server offload for mobile power

*Spend power to save powere.g., periodic cleanup to save energy, …

#How do we formalize these principles?

Building the l tisolution

Measure & modelPredict & analyzePredict & analyzeActuate & control


Measure & modelPredict & analyzeActuate & control

“Science” challenges in each category

Measure & modele.g., neural-network models for thermal maps (Consil/Weatherman)

Predict & analyzePredict & analyzee.g., models to predict power-performance-cooling (Zephyr, Zesti)

Actuate & controle.g., federated control theory, scheduling algorithms

One example[ASPLOS08][ASPLOS08]

Server iLO

Enclosure IAM

Rack

X

X

X

X

VM

heterogeneity

performance

performance

performance

X

X

X

X

X

X

Power Struggles!

Average power

Peak thermal power

Peak electrical power

CPU

iLO

X

XX

OS-wlm

OS-gwlm

SIM

Vmotion

VM-res.all

LSF

X

Local optima

global optima

performance

X

X

X

X

XCHAOS!!

(“Power” Struggle)

X

X

X

Feedback Controller @ Core

April 10, 200948

• Formal theoretical guarantees of stability and performance• Can account for inaccuracies in model, workload variations


Efficiency + capping at single server

Adapt power consumption to the local budget

Adapt the “capacity” allocation to the resource demand

Power efficiencyPower Capping

D1

EMEnclosure

budget -

rref1

P1SM1Local

budget Max

P

+Min

BLADEEC1

Dm

rrefm

PmSMmLocal

budget Max BLADEECm

Multi-level Power CappingServer Manager (SM)• Efficiency + capping

Group Manager (GM)

Enclosure Manager (EM)• Enclosure-level throttling

April 10, 2009 50

- Pgrp

+

Pencl

Groupbudget

GM Min

Min

!

DN-1

rrefN-1

PN-1SMN-1

!

ServerECN-1

DN

rrefN

PNSMN ServerECN

p g ( )• Policy-based allocation

EM

SM BLADE

SM

EC

BLADEEC

VMC

r1

rm

D1

Dm

Virtual Machine Workload Distributor

Virtual Machine Controller• Workload placement

• Power minimization

• Power budget constraints

• ‘01’ I t P i

Groupbudget GM

SERVEREC

SERVEREC

SM

SM

rN-1

rN

DN-1

DN

Workload consolidation & migrationCapping + efficiency

• ‘0-1’ Integer Programming

• Greedy heuristic

Unified and Extensible Architecture

It works!

P k P

Average Power

It works well!

65% savings (OpEx)

0 20 40 60 80 100

Performance

Peak Power

Unified Baseline

20% savings (CapEx)

Similar performance


Average Power

Other interesting insights…

No VMCVMC onlyUnified

0 20 40 60 80 100

Performance

Peak Power

Unified VMCOnly NoVMC Baseline

InterfacesFormal rigor

FlexibleFlexibleExtensible

Federated

ClosingPower mgmt ready for a scienceimportant future challenges & prior body of work

Several interesting challengesformulating the problemformulating the probleme.g., optimality, metrics

thinking about the solutione.g., principles of power management

designing the solutione.g., models, analysis, control, algorithms, …

Thanks!!

but, havent we done a lot? of course, power is important

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