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
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MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
SummaryResults
SPECTR Outline
4
MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
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
7
MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
SummaryResults
Benefits:● Simultaneouslyandrobustly trackmultipleobjectives
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MIMOControlTheoryforCoordination
Benefits:● Simultaneouslyandrobustly trackmultipleobjectives
Shortcomings:● Thegoal isfixed atdesign-time
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MIMOControlTheoryforCoordination
Theweighted TrackingErrorCostmatrix isfixed.
FPS:Power <=1:10whenMaximizingFPSunderaPowercap
Benefits:● Simultaneouslyandrobustly trackmultipleobjectives
Shortcomings:● Thegoal isfixed atdesign-time● DoesNOT scale whenhavingseveralcontrolinputsand
measuredoutputs.
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MIMOControlTheoryforCoordination
SPECTR Outline
11
MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
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
TheScalabilityChallenge:Example1
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!
TheScalabilityChallenge:Example2
Howmanyoperations areexecutedineachcontrolepochforasinglelargeMIMOcontrollingNcores?
SPECTR Outline
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MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
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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MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
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
23
MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
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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MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
SummaryResults
5stepstodesign andverify asupervisor:
SupervisoryController
SupervisorSynthesisProcess
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SupervisoryController
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.
SupervisoryController
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)
SupervisoryController
Steps3-5:SynthesisandVerification
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The synthesizer generates aminimally-restrictive controllerfor the given plant model andspecifications.
Automatically generatedandverifiedusing synchronouscompositionoperationsin
Supremica SCTtool.
SupervisoryController
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
32
MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
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
ExperimentalResults-- ContollerEvaluation
LongersettlingtimeduetolargeMIMOcontroller.
● QoStask:x264● Controller:SPECTR
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Let’sTakeacloserlookateachphase
ExperimentalResults-- ContollerEvaluation
SafePhase:QoSApponlySPECTR focusesonsatisfyingFPSwiththeminimumpower
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<5%FPSsteadystateerror(minimalwastedperfomance)
PowerbelowTDP
ExperimentalResults-- ContollerEvaluation
EmergencyPhase:TDPreducedinresponsetothermaleventSPECTR satisfiesthereferenceFPSandpower
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<5%FPSsteadystateerror
ExperimentalResults-- ContollerEvaluation
DisturbancePhase:TDPreturnedtonormal,backgroundtasksaddedSPECTR focusesonpowercapping
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NoTDPviolations,but~23%FPSsteadystateerror
(impossibletotrackwithoutviolatingTDP)
ExperimentalResults-- ContollerEvaluation
● 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
MotivationMIMOControl TheoryforCoordinatedManagementUnaddressedChallengesinResourceManagement
AutonomyScalability
SupervisoryControlTheory(SCT)viaSPECTRScalabilityand AutonomythroughSCTSPECTR OverviewCaseStudy
CaseStudyResultsSummary
SupervisorSynthesisProcess
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.
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