SPECTR - duttgroup.ics.uci.eduduttgroup.ics.uci.edu/wp-content/uploads/2018/05/SPECTR-formal... · 11 Motivation MIMO Control Theory for Coordinated Management Unaddressed Challenges

FormalSupervisoryControlandCoordinationforMany-coreSystemsResourceManagement

AmirM.Rahmani BryanDonyanavard TiagoMück Kasra MoazzemiAxelJantsch Onur Mutlu NikilDutt

SPECTR

23rd ACM International Conference on Architectural Support for Programming Languages and Operating SystemsWilliamsburg, VA, March 2018

ExecutiveSummary• Motivation:

• Formalsupervisorycontroltheory(SCT) cancombine thestrengths ofclassicalcontroltheorywithheuristicapproachestoefficientlymeetchangingruntimegoals.

• SCTenableshierarchical control andfacilitatesautomaticsynthesisofthehigh-levelsupervisorycontrolleranditspropertyverification.

• Problem:Currentresourcemanagementtechniquesdonotoffer1)robustness,2)formalism,3)efficiency,4)coordination,5)scalability,and6)autonomyalltogether.

• Goal:Addressallsixkeychallengesinheterogeneousmultiprocessors(HMPs)resourcemanagement,inparticularscalability andautonomy

• OurProposal:SPECTR usesSCTtechniquessuchasgainscheduling toallowautonomyforindividualcontrollers,andmodulardecompositionofcontrolproblemstomanagecomplexity.

• Evaluation:1. WeimplementSPECTRonanExynos platformcontainingARM’s

big.LITTLE-based HMP2. SPECTRcanmanagemultipleinteractingresources(e.g.,chippowerand

processingcores)inthepresenceofcompetingobjectives(e.g.,satisfyingQoS vs.powercapping)

3. SPECTRachievesupto8x and6x bettertargetQoS andpower trackingoverstate-of-the-art,respectively(inourcasestudy).

2

SPECTR Outline

3

MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement

AutonomyScalability

SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy

CaseStudyResultsSummary

SupervisorSynthesisProcess

SummaryResults

SPECTR Outline

4


AutonomyScalability




SummaryResults

● Severalconflicting goals/constraints

● Multipletunableknobs

● Adhocheuristics○ Canbesub-optimal○ Noformalmethodology○ Noguarantees

ResourceManagementinMany-coreSystems

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ChallengesinResourceManagement

Majoron-chipresourcemanagementapproachesandthekeyquestionstheyaddress(∗ =partiallyaddressed)

Methods Robustness Formalism Efficiency Coordination Scalability Autonomy

A Machinelearning ✔ ✔ ✔

B Estimation/Modelbasedheuristics

✔ ✔

C SISOControlTheory ✔ ✔ ✔ ✹

D MIMOControlTheory ✔ ✔ ✔ ✔

E SupervisoryControlTheory[SPECTR]

✔ ✔ ✔ ✔ ✔ ✔

Guaranteed coordination ofmultipleconflictinggoalshasbeenrecentlyaddressedforsingle-core systemsviaMIMO

controltheory[ISCA’16].Whatifgoalchangesatruntime(Autonomy)?Canweofferasystematicdesignflowforhierarchicalcontrol(Scalability)?

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TheGoal

SPECTR Outline

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AutonomyScalability




SummaryResults

Benefits:● Simultaneouslyandrobustly trackmultipleobjectives

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MIMOControlTheoryforCoordination


Shortcomings:● Thegoal isfixed atdesign-time

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Theweighted TrackingErrorCostmatrix isfixed.

FPS:Power <=1:10whenMaximizingFPSunderaPowercap


Shortcomings:● Thegoal isfixed atdesign-time● DoesNOT scale whenhavingseveralcontrolinputsand

measuredoutputs.

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SPECTR Outline

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AutonomyScalability




SummaryResults

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TheAutonomyChallenge:AnExample

Whatifthegoalchangesatruntime?

Weneedtheabilitytoswitchmodesatruntime

AMIMOcontrollerdesignedwithhigherpriorityonQoSoverpower

Poweriscapped.QoS isnotmet!

QoS ismet.Powerviolation!

AMIMOcontrollerdesignedwithhigherpriorityonpoweroverQoS

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System(x)

Black-boxIdentificationof

SystemDynamics

TheScalabilityChallenge:Example1

Whatifthe# controlinputsandmeasuredoutputs islarge?

14Weneedtolimitthesystemsize

System for 2 x 2 MIMO

Asetofworkloads

Asetofworkloads


Prediction is very accurate

Prediction greatly diverges from reality

F level 1

F level N

1 core is active

All cores are active

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Whatifthe# controlinputs andmeasuredoutputs islarge?

System(x)

Controller

TheScalabilityChallenge:Example2Controller

DesignComplexity

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Usingonelargecontrollerisnotfeasible!


Howmanyoperations areexecutedineachcontrolepochforasinglelargeMIMOcontrollingNcores?

SPECTR Outline

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AutonomyScalability




SummaryResults

UsingSupervisoryControlTheorywe…

● Provideautonomy viaadaptation inresponsetochangesinpolicy○ Computecontrolparametersfordifferentpolicies

offline

● Providescalability viadecomposition ofsystemintomultiplesubsystemsorganizedinahierarchy○ Supervisor provideshigh-levelmanagement

SPECTR

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ScalabilityviaSupervisoryControl

High-levelPlantModel(Phi)

Plant(Plo)

SupervisoryController(Chi)

Low-levelController(Clo)

Comhi_lo

Conlo

Inflo

Inflo_hi

Conhi

Infhi

High-level Virtual Control

Combininglogicwithdiscrete

dynamics

19Low-levelTraditionalControlLoop

Representsanabstraction ofthePlant

Informationchanneltoupdatethehigh-levelmodel

SupervisoryControllerusesthischanneltocontrolthehigh-levelmodel

Theactual controlhappensviadiscreteevent-basedcommands

AutonomyviaSupervisoryControl

ThisSCTtechnique iscalledGainScheduling

FPS:Power <=1:10

FPS:Power <=10:1

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Controlparameterspre-designedtoprioritize onemeasured

outputovertheother(s)

Trackingpower is10xmoreimportant than

trackingQoS

TrackingQoS is10xmoreimportant than

trackingpower

SPECTR Outline

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AutonomyScalability




SummaryResults

Sub-plant1 Sub-plant2 Sub-plantN

SupervisoryController

VariableGoalsandPolicies

High-levelPlantModel

ClassicController1

ClassicController2

ClassicControllerN

Userinputs

Con_lo1

Inf_lo1

Con_lo2

Inf_lo2

Con_loN

Inf_loN

Gains1 Refs1Gains2 Refs2

GainsN RefsN……

……

Con_hiInf_hi

Inf_lo_hiLeaf

Cont

rolle

rs

Phys

ical

Plan

t

Systemevents

SPECTR

SPECTRoverview

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Puttinghierarchicalcontrolandgainschedulingtogether!

Thesupervisorupdatesgoalsandallocatesresourcesatruntime

SPECTR Outline

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AutonomyScalability




SummaryResults

● 8-corebig.Little HMP● Twosetofapplications:

○ AforegroundapplicationwithQoSrequirements(e.g.,FPS)

○ AnumberofbackgroundapplicationswithnoQoSrequirements

CaseStudy

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ODROID-XU3 platform contains an Exynos 5422 Octa-core SoC

● Controlknobs:per-cluster DVFS,numberofidlecores● Systemgoals:

○ MeettheQoSrequirementoftheforegroundapplication○ Ensurethetotalsystempower alwaysremainsbelowthe

ThermalDesignPower(TDP)○ Minimizeenergyconsumption

CaseStudy

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SPECTR Outline

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AutonomyScalability




SummaryResults

5stepstodesign andverify asupervisor:



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Step1:PlantModel

28Manuallymodeledsub-plants Synthesizedplant

MultiplecharacteristicsareautomaticallysynthesizedusingSCTtools

E.g.Supremica

To develop possible actions inhigh-level plant model in a form ofdiscrete-event dynamics

PrioritizeQoS:Thepowerreference isupdatedtomeettheQoS referenceinanenergy-efficientmanner.

Powerbudgetviolationgeneratesacritical eventandresultsingainswitchingtowardsthepower-drivengoal.


Step2:IntendedBehaviorSpecification

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ForbiddenState

A specification defines theaccepted and forbidden states viarestrictions on the behavior of theplant model.

This examplespecificationprevents exceedingthe power budget forno more than threecontrol intervals.

Note:ThemodelinStep1hasno limitations!(e.g.,onexceedingthepowerbudget)


Steps3-5:SynthesisandVerification

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The synthesizer generates aminimally-restrictive controllerfor the given plant model andspecifications.

Automatically generatedandverifiedusing synchronouscompositionoperationsin

Supremica SCTtool.


Steps3-5:SynthesisandVerification

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SCTtools(e.g.,Supremica)alsoverify thenon-blocking andcontrollability propertiesofthesyntheziedcontroller.

Non-blocking: Acceptedstates(e.g.,idealstates)canalwaysbereached.

Stable

Controllability: Thereisapathtotheacceptedstatesfromeveryothervalidstate.

SPECTR Outline

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AutonomyScalability




SummaryResults

● QoSapplications:○ PARSECapplications:x264,bodytrack,canneal,

streamcluster○ Data-intensivemachinelearningworkloads:k-

means,KNN,leastsquares,linearregression

● ComparedSPECTRwiththreealternativeresourcemanagers○ MM-Pow: 2x2MIMOs(onepercluster)with

gainsoptimizedtotrackpower○ MM-Perf: 2x2MIMOs(onepercluster)with

gainsoptimizedtotrackperformance/QoS○ FS:singlesystem-wide 4x2MIMOwithgains

optimizedtowardspower

Evaluatedresourcemanagerconfigurations

Fixed-Objective

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Executionscenariowiththreephases(x264):

1. Phase1- SafePhase:only theQoS applicationruns;powerlimitedbyTDP

2. Phase2- Emergencyphase:powerlimitsetto1WbelowTDPtoemulateathermalemergency

3. Phase3- Workloaddisturbancephase:powerlimitrestoredtoTDP,butnowseveralbackgroundtasksstart,interferingwiththeQoSapplication

ExperimentalResults– ContollerEvaluation

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EnoughpowertotrackQoS

Onlyfeasibletotrackpower

TrackQoSunderapowercap

MM-Pow

MM-Perf

MM-FS

SPECTR

● QoStask:x264● Controller:MM-Pow(power-oriented)

○ 2x2MIMOs(onepercluster)withgainsoptimizedtotrackpower

ExperimentalResults-- ContollerEvaluation

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~40%morethennecessaryFPSinPhase1

Wastingenergy!

UnderapowercapWastedperformance

ItworksfineinPhase2 and3 byfocusingonpowercapping!

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ExceedsTDPby~30%inPhase3!

TrackingFPS Exceedingpowerlimit

ExperimentalResults-- ContollerEvaluation● QoStask:x264● Controller:MM-Perf(performance-oriented)

○ 2x2MIMOs(onepercluster)withgainsoptimizedtotrackQoS

ItworksfineinPhase1 and2 byfocusingonQoS tracking!

● QoStask:x264● Controller:FS(large4x2power-oriented)

○ Singlesystem-wide 4x2MIMOwithgainsoptimizedtowardspower

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~40%morethannecessaryFPSinphase1(akintoMM-POW)

SluggishresponseWastedperformance


LongersettlingtimeduetolargeMIMOcontroller.

● QoStask:x264● Controller:SPECTR

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Let’sTakeacloserlookateachphase


SafePhase:QoSApponlySPECTR focusesonsatisfyingFPSwiththeminimumpower

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<5%FPSsteadystateerror(minimalwastedperfomance)

PowerbelowTDP


EmergencyPhase:TDPreducedinresponsetothermaleventSPECTR satisfiesthereferenceFPSandpower

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<5%FPSsteadystateerror


DisturbancePhase:TDPreturnedtonormal,backgroundtasksaddedSPECTR focusesonpowercapping

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NoTDPviolations,but~23%FPSsteadystateerror

(impossibletotrackwithoutviolatingTDP)


● Accuracy oftheidentifiedsystemmodelsofdifferentsizedMIMOcontrollers

● Amodeloutputwithintheconfidenceinterval indicatesthatthedeterministic componentofthemodeloutputwillbenearthetrueoutput.

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ExperimentalResults-- Scalability

Confidencelevelof99% Confidencelevelof99%

Outsidetheconfidenceinterval

Black-boxsystemidentificationisnotfeasibleforlargeandcomplexMIMOsystems!

Withintheconfidenceinterval

● AdetailedContollerEvaluationon:○ PARSECapplications:bodytrack,canneal,streamcluster○ Data-intensivemachinelearningworkloads:k-means,

KNN,leastsquares,linearregression

● ModelAccuracyAnalysisofdifferentsizedMIMOcontrollers:○ 2x2->feasibleandefficient○ 4x2->feasiblebutsluggish○ 10x10->notfeasible

● Furtherdiscussionon:○ Controllerresponsiveness(settlingtime)○ Controllerstability 43

OtherResultsinthePaper

SPECTR Outline

44


AutonomyScalability




SummaryResults

● Resourcemanagersneedtooffer 1)robustness,2)formalism,3)efficiency,4)coordination,5)scalability,and6)autonomyalltogether

● SPECTR offersthemall!○ SPECTRadapts to changinggoalsatrutime○ SPECTR decomposesthecontrolproblemsto manage

itscomplexity

● SPECTR achieves upto8x and6x bettertargetQoS andpower tracking overstate-of-the-art,respectively(inourcasestudy)

● SPECTRisapplicabletoanyresourcetypeandobjectiveaslongasthemanagementproblemcanbemodeledusingdynamical systemstheory

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Summary

FormalSupervisoryControlandCoordinationforMany-coreSystemsResourceManagement

AmirM.Rahmani BryanDonyanavard TiagoMück Kasra MoazzemiAxelJantsch Onur Mutlu NikilDutt

SPECTR

23rd ACM International Conference on Architectural Support for Programming Languages and Operating SystemsWilliamsburg, VA, March 2018

47

Step1

Step2

Step3

Step9

Step6

Step5

Step7 Step8

SPECTRDesignFlow

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Steady-stateErrorforAllBenchmarks

Steady-stateerrorforallbenchmarks,groupedbyphase.Anegativevalueindicatestheamountofpower/QoS exceedingthereferencevalue(bad),apositivevalueindicatestheamountofpowersaved(good)orQoS degradation(bad)

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ModelAccuracy• Autocorrelationof

residualsforidentifiedsystemmodelsofdifferentsizedMIMOcontrollers.

• Weshowasingleperformanceandpoweroutputforeachmodeledsystemacrossmultiplesampleinputs.

Documents

SPECTR - duttgroup.ics.uci.eduduttgroup.ics.uci.edu/wp-content/uploads/2018/05/SPECTR-formal... · 11 Motivation MIMO Control Theory for Coordinated Management Unaddressed Challenges