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Energy Efficiency Op-miza-on with GEOPM
JonathanEastep[[email protected]]PrincipalEngineer,PhD
12November2017
h9p://geopm.github.io/geopm
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§ GEOPMoverview,use-cases,andstatus§ Intel,LRZ,LLNL,ArgonnecollaboraMon§ Newexperimentalresults§ CollaboraMonnextsteps
Outline
IntelCorporaMon
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§ RunMmeforin-bandpowermanagementandopMmizaMon§ On-the-flymonitoringofHWcounters&applicaMonprofiling§ Feedback-guidedopMmizaMonofHWcontrolknobse[ngs
§ Opensourceso\ware(flexibleBSDthreeclauselicense)
§ Extensiblethroughpluginarchitecture§ AddnewenergyopMmizaMonstrategies§ Addsupportfornewarchitecturesbeyondx86(trulyopen)
§ DesignedforholisMcopMmizaMon§ Job-wideglobalopMmizaMonofHWcontrolknobse[ngs§ ApplicaMon-awarenessformaxspeeduporenergysavings
§ Scalableviadistributedtree-hierarchicaldesign,algorithms
MPI Comms Overlay Shared Mem Region
Power-Aware RM / Scheduler
GEOPM Controller
SHM
GEOPM
GEOPMRoot
GEOPMAggregator
GEOPMAggregator
GEOPMLeaf
Msr-safe (or Other Drivers for Non-x86 PlaPorms)
MSR
MPI Ranks 0 to i-1
GEOPMLeaf
Processor
MPI Ranks i to j-1
Processor
MPI Ranks j to k-1
GEOPMLeaf
Processor
MPI Ranks k to n-1
GEOPMLeaf
Processor
Projecturl:hdp://geopm.github.io/geopmContact:[email protected]
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§ Turn-key(requiresnoappannotaMon):§ AutomaMconlinejobprofiling
§ Node-level:tracesamplesofprocessorcountersandcorrelateHWacMvitytoeachOpenMPparallelregion
§ Job-level:aggregatetheenergycountersacrossalljobcomputenodestomonitoroveralljobpowerorenergy
§ AutomaMcofflineoronlineopMmizaMon§ Willtalkmoreaboutthistoday
§ OfflinevisualizaMonofprofiledata§ Pythonscriptsleveragingpandasfordataanalysis§ Helpfulfordebuggingnewpluginsorunderstanding
howtheyopMmizeenergyorrunMme§ Plottraceofplugindecisionsanddatathey’rebasedon
GEOPM Use Cases
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§ Advanced(requiresusingGEOPMprofilingAPIforappannotaMon):§ AutomaMconlinerebalancing
ofpower&perfamongnodes§ Purpose:acceleratecriMcalpathnodes
inMPIbulk-synchronousapplicaMons§ RefertoISC’17paperonGEOPMby
Eastepetal.formoreinfo§ Note:workinprogresstomakethe
annotaMonautomaMc/turn-keytoo
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GEOPM Community (1) Ins$tu$on PrincipalInves$gator Project
NameProjectScope Contribu$on
TypeTimeSpan
QualityLevel
Funded?
Argonne KalyanKumaranVitaliMorozov
CORAL 1.GEOPM1.0productdevelopment Sponsor Q2’15–Q4’17
Product Yes
IBMSTFC–Hartree
VadimElisseevMilosPuzovicNeilMorgan
1.GEOPMporttoPower8+NVLink2.IntegraMonofGEOPMwithEAS
Contributor Q4’16–TBD
Research Yes
LLNL BarryRountreeAniruddhaMarathe
CRADA 1.IntegraMonofGEOPMandConductorrunMmetech2.StudiestomoMvateGEOPM/HWcodesign
Contributor Q3’13–TBD
Research Yes
LLNLU.ofArizonaArgonne
TapasyaPatkiDaveLowenthalPeteBeckman
ECPPSECPArgo-GRM
1.ExascalepowerstackleveragingGEOPM2.IntegraMonofGEOPM+Caliperframework3.IntegraMonofGEOPMwithEAS4.PortofGEOPMtonon-x86architecture
Contributor Q1’17–Q4’19
Near-Product
Yes
LRZ DieterKranzlmüllerHerbertHuberTorstenWilde
1.EnergyopMmizaMonpluginforGEOPM1.02.PowerramplimiMngpluginforGEOPM1.x
Contributor Q3’17–Q4’20
Near-Product
Yes
Sandia JamesLarosRyanGrant
PowerAPI
1.GEOPMandPowerAPIxfacecompaMbility2.PowerAPIcommunityWGkickoffatIntel
User Q4’14-TBD
IndustryStandard
Yes
*
*
*=collaboratorwillbesharingtheirGEOPMusagesandexperiencesatSC17:BoFonPowerAPI,GEOPM,andRedfish
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GEOPM Community (2) Ins$tu$on PrincipalInves$gator Project
NameProjectScope Contribu$on
TypeTimeSpan
QualityLevel
Funded?
Argonne KalyanKumaranVitaliMorozovKevinHarms
1.GEOPM>1.0featuredevelopment2.GEOPMenablementforsystempowercapping+EAS3.StudiestomoMvateGEOPM/hardwarecodesign
Sponsor Q1’18–Q4’21
Product WIP
CINECA CarloCavazzoni 1.SystemlevelrunMmeforpowercappingandpowerramplimiMngleveragingGEOPM
Contributor Q2’18–Q1’21
Near-Product
WIPꝉ
IT4I LubomirRiha 1.GEOPMportstoOpenPOWERandARM2.ExtensionstoGEOPMapplicaMonprofiler3.IntegraMonofGEOPMwithEAS
Contributor Q2’18–Q1’21
Near-Product
WIPꝉ
E4 FabrizioMagugliani 1.GEOPMporttoOpenPOWER Contributor Q2’18–Q1’21
Near-Product
WIPꝉ
PNNL LeonSong 1.GEOPMextensionstotunenewHWcontrolknobse[ngs2.GEOPMextensionsforcoordinatedtuningofSWparamsandHWcontrolknobse[ngs
Contributor Q1’19–Q4’20
Research WIPꝉ
ꝉ=lederofintentorequivalentin-hand(non-binding)
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GEOPM Release Schedule
AlphaQ2’17
BetaQ2’18
v1.0Q4’18
Commitment:
AlphaQ2’17
BetaQ1’18
v1.0Q2’18
StretchGoal:
TOSS3.x
ISC’18 SC’18
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Announcement:OpenHPCapplicaMonhasbeensubmided.UnderconsideraMon.
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§ Presentfocus:§ DevelopnewtechniquesinGEOPMtoimproveenergy-to-soluMonw/modestimpacttoMme-to-soluMon§ WorktowardintegraMngGEOPMandthesetechniquesintoproducMonsystemsatLRZ,LLNL,andArgonne
§ EnergyopMmizaMonapproach:§ AutomaMcallyadaptprocessorcorefrequencybasedoncharacterisMcsofindividualapplicaMons§ Runthecoresslowertosaveenergyiftheappisbodleneckedbythememoryornetworksubsystems§ Adaptfrequencyatafine-grainedMmescale:differentfrequencyforeachcomputaMonalphaseintheapp§ It’scriMcaltoadapttophasessinceeachphase’srunMmecanhavewildlydifferentfrequencysensiMvity
§ InnovaMonvspriorart:§ Novelper-phase-adaptaMonenablesbiggerenergysavingsandlowerimpacttoMme-to-soluMon
Intel, LRZ, LLNL, and Argonne Collab
Bigwinsarepossible:upto16.5%energysavingsat0.3%increasein$me-to-solu$onStatus:trendingtocompletethisworkinMmeforGEOPMBetarelease
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Two Techniques Under Development
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§ Startsimple:offlineautoma*cfrequencyopMmizaMonviascriptsandDeciderplugin§ GEOPMplugintakesineachphase’sfrequencyas
aninput,appliestheinpudedfrequencyuponphaseentry
§ Offline,foragivenapplicaMon,scriptssweepoverpluginfrequencyconfiguraMonstocharacterizetheapp’sphases,idenMfybestfrequencies(minenergy@<10%execuMonMmeimpact)
§ EachMmeappislaunched,itrunswithbestphasefrequencyconfiguraMonsidenMfiedbythescripts
§ OfflineapproacheslikethishaveknownlimitaMons:§ TheybreakdownwhenphaseexecuMonMmevs
frequencyscalingdependsonrunMmefactors
§ Getfancy:onlineautoma*cfrequencyopMmizaMonviaDeciderplugin§ GEOPMplugincharacterizestheapplicaMon
onlineandtuneseachphase’sfrequencyduringaniniMal“learning”period
§ Whenfinishedlearning,pluginusesbestfrequencyforeachphaseandreapsenergybenefitsfortherestofexecuMon
§ Onlineapproacheslikethishavetradeoffs:§ They’rerobustagainstcaseswhenphase
execuMonMmevsfrequencyscalingdependsonrunMmefactorsandthebestphasefrequencycan’tbedeterminedreliablyoffline
§ Theybreakdownwhenlearningoverheadisanon-trivial%oftotalexecuMonMme(notcommoncaseinlong-runningHPCapps)
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Experimental Setup: Measurement
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§ CommonmeasurementmethodologyusedforevaluaMngbothtechniques
§ ProgrammersinstrumentapplicaMonphaseentry/exitwithaprofilingAPIprovidedwithGEOPM§ APIisdesignedtobelightweightandeasyforprogrammerstouse§ Nonetheless,workunderwaytoautomatephaseinstrumentaMonusingOMPT
§ GEOPMrunMmeperformsenergyandexecuMonMmemeasurements§ UsesthephaseinstrumentaMontotrackphaseentryandexitMmestamps§ Periodicallysamplesenergyusingprocessorcounters(orothermeans)§ IncorporatesaccounMnglogictotracktotalphaseenergyandexecuMonMmebasedonabove§ GEOPMreportfeatureoutputsthisper-phasedatatotextfileoneachnode
§ Alldatapointsinchartssharedtodayrepresentanaverageofatleast7trials§ Foreachtrial,resultsaveragedacrossnodessinceresultsvaryfordifferentnodes
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Experimental Setup: Workloads
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• Studiedworkloadsincluding:• Proxyapp:modelbulk-synchronousapplicaMonwithconfigurablebalanceofDGEMMand
STREAMcomputaMon(fromGEOPMtutorialsandintegraMontests)• FT:adiscrete3-dFFTkernel(fromNASParallelBenchmarksuite)• miniFE:finiteelementcode(fromCORALprocurementbenchmarks)• Nekbone:thermalhydraulicscode(fromCORALprocurementbenchmarks)
• AppliedthefollowingconvenMonswhenconfiguringworkloads:• SizedtheproblemtofitwithinavailableDRAMonthenode• TestedseveralconfigsofMPIranksandOpenMPthreadsperrank;usedtheconfigwithlowest
Mme-to-soluMon• SetapplicaMonaffinitymaskstostayoffofCPU0tominimizeOSjidereffects• SetGEOPMcontrollerprocessaffinitytoCPU1(applicaMonaffinitysettostayoffofthisCPU)• Didnotusehyperthreads
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Experimental Setup: Hardware
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JLSEBroadwellXeonCluster(Argonne)
QuartzBroadwellXeonSystem(LLNL)
WorkloadconfiguraMonunderstudy
proxyapp:used1instancepernodewithnointer-nodecommunicaMon
FT,miniFE,Nekbone,Gadget:allusedmulM-nodeconfiguraMonswithMPIcommunicaMon
Processorandmemoryspecs
4x44-corenodes(dual-socket),Broadwellserverprocessors,128GBofDRAM
Upto8x36-corenodes(dual-socket),Broadwellserverprocessors,128GBofDRAM
Processorcorefrequencyrange
1.2GHzto2.2GHzsMcker,Turboisenabled:frequencymayexceedsMcker
1.2GHzto2.1GHzsMcker,Turboisenabled:frequencymayexceedsMcker
Networkhardware N/A IntelOmniPathHFIsandswitches
So\wareenvironment RHEL7.4Linuxdistro,IntelP-statedriverrunningsetto‘performance’withmin=max=sMcker,Intelcompilertoolchain,IntelMPIimplementaMon
RHEL7.3LinuxdistrowithIntelP-statedriverdisabled,legacygovernorsetto‘performance’,Intelcompilertoolchain,MVAPICH2MPIimplementaMon
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Experimental Setup: 3 Inves-ga-ons
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1. OpportunityAnalysis• Useproxyapp(parameterizedmodelapplicaMon)todetermineenvelopeofenergy-to-soluMonand
Mme-to-soluMonimpactwe’llseeoverthelandscapeofBSPapplicaMons• Measureenergy-to-soluMondecreaseandMme-to-soluMontradeoffrela$vetorunningats$ckeron
theJLSEclusteratArgonne• Comparetwodifferentuse-casesfortheofflinetechniquewedeveloped:
• ‘OfflineautomaMcapplica*onbest-fit:’allphasesrunatcommonfrequency(best-fitacrossall)• ‘OfflineautomaMcper-phasebestfit:’eachphaserunsatthebestfrequencyforit
2. BenchmarkofflineenergyopMmizaMontechnique• TargetFT,miniFE,andNekboneworkloads• SameasabovebuttargetslesssyntheMcworkloadsandperformsexperimentsonLLNLQuartzsystem
3. BenchmarkonlineenergyopMmizaMontechnique• TargettheproxyappandperformexperimentsonJLSEclusteratArgonne• Comparetheonlineandofflinetechniqueswedeveloped:
• ‘OfflineautomaMcper-phasebest-fit:’scriptsidenMfybestfrequencyviaofflinecharacterizaMon• ‘OnlineautomaMcper-phasebestfit:’GEOPMpluginperformscharacterizaMon/tuningonline
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Results: Opportunity Analysis
BigenergysavingsarepossiblewithfrequencyopMmizaMoninGEOPMvsrunningworkloadsatsMcker:upto16.5%energysavingsat0.3%increasein$me-to-solu$on
Withper-phaseopMmizaMon,energysavingsincreasewithincreasein%Mmeinmemory-limitedphasePer-phaseopMmizaMonsimultaneouslyoffersbederenergy-to-soluMonANDMme-to-soluMonversus
opMmizingfrequencyacrosstheblendedcharacterisMcsofallapplicaMonphases
0
5
10
15
20
18% 32% 40% 49% 56% 64% 75%
%decreaseinene
rgy-to-soluM
on
%MmeinSTREAMphase
Energy-to-SoluMonDecreaseofflineautoapplicaMonbest-fitofflineautoper-phasebest-fit
-2
0
2
4
6
8
10
18% 32% 40% 49% 56% 64% 75%
%increaseto
Mme-to-soluM
on
%MmeinSTREAMphase
Time-to-SoluMonIncreaseofflineautoapplicaMonbestfitofflineautoper-phasebest-fit
1.1E+09
1.3E+09
1.5E+09
1.7E+09
1.9E+09
2.1E+09
2.3E+09
18% 32% 40% 49% 56% 64% 75%
best-fitfrequ
ency(H
z)
%MmeinSTREAMphase
OfflineAutoAppBest-FitFrequency
DGEMMBest-Fit
STREAMBest-Fit
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Results: Offline App vs Per-Phase Best-Fit
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Energy-to-SoluMonandTime-to-SoluMonComparisononQuartzOfflineAutomaMcApplica*onBest-Fit OfflineAutomaMcPer-PhaseBest-Fit
Workload EtSDecreasevsSMcker
TtSIncreasevsSMcker
EtSDecreasevsSMcker
TtSIncreasevsSMcker
FT 9.5% 6.8% 15.8% 4.8%
miniFE 8.5% 5.8% CollecMngdatanow CollecMngdatanow
Nekbone 7.9% 2.4% CollecMngdatanow CollecMngdatanow
ResultsstarMngtoconfirmthatGEOPMprovidesbenefitsforanumberofworkloadsbeyondourproxyapp
Moredataontheway,butdatastarMngtosuggestper-phasefrequencyopMmizaMonsimultaneouslyoffersbederenergy-to-soluMonANDMme-to-soluMonvsopMmizingfrequencyacrossblendedcharacterisMcsofwholeapp
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Results: Online vs Offline Technique
Remember,offlineapproachisbridle.Thegoal:same(orbeder)resultsviamorerobustonlineapproachWethinkmuchoftheEtSandTtSgapcanbeclosedviaaddressingfrequencylatency&doinglongerrunsFine-tuningneeded,butalreadyseeingpromisingdecreasesinenergy-to-soluMonwithonlineapproach
ExplanaMonofEtSandTtSgaps:• Runswereshorterthanrealapps
->noMceable“learning”overhead• Reduced#samplesinlearning
periodtoreduceoverhead->morenoise-relatedcontrolerrors
• Observedlatencybetweenfrequencychangerequestsandenactment(10sofmilliseconds)->notrunningatdesiredfrequencyimmediately,confusingalgorithm
0
2
4
6
8
10
12
14
16
18
18% 32% 40% 49% 56% 64% 75%%decreaseinene
rgy-to-soluM
on
%MmeinSTREAMphase
Energy-to-SoluMonDecrease
onlineautoper-phasebest-fitofflineautoper-phasebest-fit
-2
0
2
4
6
8
10
18% 32% 40% 49% 56% 64% 75%%increaseto
Mme-to-soluM
on
%MmeinSTREAMphase
Time-to-SoluMonIncrease
onlineautoper-phasebest-fitofflineautoper-phasebestfit
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Next Steps
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1. Extendonlineautoper-phaseenergyopMmizaMonplugin• LeverageGEOPMfeatureforautomaMcdetecMonofOpenMPparallelregion
entry/exittoremoveneedforprogrammertoinstrumentphaseswithAPI
2. ExpandevaluaMonstoincludemorebenchmarks(e.g.Gadget@LRZ)andmorearchitectures(e.g.ThetaKnightsLandingsystem@Argonne)
3. PolishtheenergyopMmizaMontechniquesdemonstratedtodayandincludetheminGEOPMBeta
4. WorkwithLRZ,LLNL,andArgonnetowarddeploymentontheirproducMonsystems
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GEOPM Core Team Acknowledgements
HardwareTeam:• ProcessorFirmware
• RevathyRajasree
• HardwareArchitectureandDesign• FedeArdanaz• FuatKeceli• KellyLivingston• LowrenLawson
So\wareTeam:• GEOPMDevelopment
• ChrisCantalupo• DianaGudman• BradGeltz• BrandonBaker
• Research• SidJana• AsmaAl-Rawi• MadhiasMaiterth
LeadArchitect:• JonathanEastep,PrincipalEngineer
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Per-Phase Best-Fit Frequency Table for FT
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Widerangeinbest-fitfrequencyacrossphases
CodeinspecMonconfirmsthephasesthattolerate1.2GHzarememory-limitedorMPIphases,asexpected
PhasestoleraMng1.2GHzuseroughlythesame%oftotalexecuMonMmeasphasesneeding2.0-2.1GHz
Runningallphasesat2.0-2.1GHzwouldwasteenergyinTRANSPOSE,MPI,andEVOLVEphaseswithlidlebenefittoexecuMonMme
Runningallphasesat1.2GHzwouldharmexecuMonMmeofFFTandINDEX_MAPphasesbymuchmorethan10%
Thesefactsillustratewhyitissub-opMmaltoapplythesamefrequencyacrossallphases
FTPhase BestFrequency
%TotalExecTimeatS$cker
Transpose_1 1.2GHz 06.7%
Transpose_2 1.2GHz 06.8%
MPI_All2All 1.2GHz 35.2%
EVOLVE 1.2GHz 09.9%
FFT_1_2 2.0GHz 13.1%
FFT_1 2.0GHz 13.1%
FFT_2 2.1GHz 16.0%
INDEX_MAP 2.1GHz 0.03%
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Energy May Not Monotonically Increase • Onlinealgorithmcan’tsimplywalkdownfrequencyunMlthere’sa10%TtSincrease(relaMvetosMcker)
• Reason:reducingfrequencybelowsMckerincreases(notdecreases)energyforDGEMMphase;naïvealgorithmswouldchooselowerfrequencyforDGEMMthanisopMmalfromanenergyperspecMve!
166001680017000172001740017600178001800018200184001860018800
0
20
40
60
80
100
120
energy(J)
runM
me(s)
frequency(Hz)
DGEMMPhaseScaling
runMme
energy
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1. At-scaleloadimbalanceduetomanufacturingvaria$oninpower-cappedsystems.ThisproblemisdeemedoneofthekeyExascale-erapowerchallenges.DevelopingGEOPMandtechniquestoaddressthisproblemoverthepast6yearsmademeaPrincipalEngineeratIntel.
2. Gapincommunityenergymanagementresearchtools.Therewaspreviouslynopla�ormforenergymanagementresearchthatwasopen,scalable,robust,flexible,portable(trulyopen),andbackedbyseriousengineeringresources.NowthecommunityisusingGEOPM,porMngtonon-x86architectures,integraMngtheiropMmizaMontechniquesintoit,andintegraMngitwithotherso\warecomponents.
3. Gapinindustryserverpowermanagementroadmapsandtechnicaldirec$ons.Powermanagementwaspreviouslydonenode-locally.Techniqueswereoblivioustoapplica3on-levelinforma3onsuchasbodlenecksonremotenodesthatcouldlimitoverallperformanceandwereunabletoforecastwhatcomputaMonwasgoingtohappeninthefutureandopMmizepower-performancepolicyaccordingly.GEOPMaddsacriMcallayerofglobalopMmizaMonacrossnodes,applicaMonandapplicaMonphaseawareness,andforecasMngcapabiliMes.SeeISC’17paperfordemoofbenefits(upto30%speedup).
What Problems Does GEOPM Address?
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GEOPM Interfaces and HPC Stack Integra-on
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§ GEOPM=job-levelpowermanager§ CoordinatesopMmizaMonofhardwarecontrol
knobse[ngsacrossallcomputenodesinjob§ Userspaceso\ware;accesstocontrolknobs
facilitatedviaOSdriverlikemsr-safefromLLNL§ Supportedcontrolknobs:nodepowercaps,
processorfrequencycontrols,morecoming
§ RunGEOPMviajoblaunchwrappers§ Includeswrappersforsrunandaprunsofar§ Samesyntaxbutwithaddedflagstoconfigure
geopm;e.g.:powerbudgetandplugin§ AdmincanprovidedefaultsviaJSONconfigfile
§ IntegrateswithRMandscheduler§ Near-term:GEOPMcanstandalone§ Long-term:integrateswithemergingSystem
PowerManager(SPM)runMmecomponent§ GEOPMinterfacetosystempowermanager
§ Feedback:GEOPMreportsjobpowerconsumpMonandotherjobcharacterisMcs
§ Control:SPMdynamicallyreconfiguresjobpowerbudgetand/orGEOPMplugin
Power-Aware Resource Manager / Scheduler
GEOPM - Resource Manager Interface
Job Power Manager GEOPM
Processor PM and Perf Counter Interfaces
GEOPM Application Profiling Interface
(Optional)
Job Launch Wrappers
PM Interfaces for System-Level
Resources
3rd parties
Intel GEOPM Team
Intel PM Arch Team
Research / Future Work
System Power Manager Runtime
JSON Config File with Defaults
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§ SeeGEOPMISC’17paperbyEastepetal.fordetailsofexperimentalsetupandfurtheranalysis§ Paperdemonstratespowerbalancingplugin:itleveragesannotaMonofapplicaMon’soutersynchronizaMonlooptodetect
criMcalpathnodesandthenreallocatespoweramongnodesinordertoequalizetheirMmetocompletealoopiteraMon§ ComparedoverallMme-to-soluMonwhencappingjobpoweron12-nodeKNLclusterwithpowerbalancerplug-invs.staMc
uniformpowerdivision(baseline);sweptoverarangeofdifferentjobpowercaps§ Regionofinterestinjobpowercaps:low-endofjobpowercapswasselectedtoavoidinefficientclockthrodlingandthehigh-
endofthejobpowercapsequalstheunconstrainedpowerconsumpMonoftheworkload§ Mainresult:upto30%improvementinMme-to-soluMonatlowendofcaps(miniFE,CoMD,AMG),withupto9-23%forthe
rest.Improvementgenerallyincreasesaspowerismoreconstrained
Results: Inter-Node Power Balancing Use Case
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Take-awaypoints:• Resultsdemonstraterobustnessofpower
balancingalgorithmagainstMme-varyingamountsofworkintheouterloopandsharpshi\sincomputaMonal-intensity(topgraphs)
• Node8,withlowestpowerefficiencyinourKNLcluster,isallocatedmorepower(middlegraphs)
• PowerbalancingalgorithmimprovescriMcalpathloopMmebyfindingthepowerallocaMonthatroughlyequalizesthefrequenciesofallnodes(bodomgraphs)
GEOPMSpeedupAnalysis(usingincludedGEOPMTraceandPythonVisualizaMonTools)
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§ GEOPMprojectisnotjustaso\wareproject.ItalsodrivescodesignofthefeaturesinIntelhardwareforpower-performancemonitoringandcontrol
§ Goalsaretosignificantlyadvancethestate-of-the-artinHPCpowermanagementtechnologyandtoensureGEOPMrunsbestonIntel
§ Researchareas:§ Processor:improvementstogranularity,reacMonMme,andinterfacesforexisMngfeatures§ Processor:hooksforGEOPMtoguideallocaMonofTurboheadroomamongcores§ Memory:hooksforGEOPMtohinttomemcontrollerwhenit’sbesttoenterlow-powerstates§ Network:hooksforGEOPMtoesMmatepower,managetradeoffsbetweenpowerand
bandwidthinHFIandswitches,andhinttoHFIwhenit’sbesttoenterlow-powerstates
Research on GEOPM/HW/FW Codesign
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§ GEOPMso\warepackageisopensource,providesarichfeaturesetfreeofcharge
§ IntentisforIntel’sfutureworkontheso\waretobeopensourceaswell§ 3rdparMesareabletomakeproprietaryextensionsofGEOPM(BSD3-clauselicense)
§ EnablesintegratorslikeDell/Cray/HPEtodevelopcommercialfor-profitplugins(i.e.addpowermanagementsecretsaucetodifferenMateyoursystemsvsthecompeMMon)
§ GEOPMteamcanhelpintegratorswiththisinaconsulMngcapacity
§ Intelcanexploredevelopingcustomprocessorfirmwareenhancementsforcustomers§ EnablesprocessorpowermanagementfirmwareandGEOPMpluginstobeco-opMmizedfor
individualcustomerneeds§ Enablesmanagementofhardwarecontrolknobse[ngswhicharenot(yet)publicallyavailable§ ProvidingGEOPMNREfundinginasystemcontractisagoodwaytoestablishsuchanengagement
GEOPM New Business Opportuni-es
IntelCorporaMon
InquirewithJonathanEastepformoreinformaMon:[email protected]