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June2023
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadership
ReferenceArchitecture
FeaturingNVIDIADGXH100Systems
RA-11333-001v6
BCM3.23.05
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|i
Abstract
TheNVIDIADGXSuperPOD™withNVIDIADGX™H100systemsisthenextgenerationofdatacenterarchitectureforartificialintelligence(AI).DesignedtoprovidethelevelsofcomputingperformancerequiredtosolveadvancedcomputationalchallengesinAI,highperformancecomputing(HPC),andhybridapplicationswherethetwoarecombinedtoimprovepredictionperformanceandtime-to-solution.TheDGXSuperPODisbasedupontheinfrastructurebuiltatNVIDIAforinternalresearchpurposesandisdesignedtosolvethemostchallengingcomputationalproblemsoftoday.SystemsbasedontheDGXSuperPODarchitecturehavebeendeployedatcustomerdatacentersandcloud-serviceprovidersaroundtheworld.
Toachievethemostscalability,DGXSuperPODispoweredbyseveralkeyNVIDIAtechnologies,including:
>NVIDIADGXH100system—toprovidethemostpowerfulcomputationalbuilding
blockforAIandHPC.
>NVIDIANDR(400Gbps)InfiniBand—bringingthehighestperformance,lowest
latency,andmostscalablenetworkinterconnect.
>NVIDIANVLink—networkingtechnologiesthatconnectGPUsattheNVLinklayerto
provideunprecedentedperformanceformostdemandingcommunicationpatterns.
TheDGXSuperPODarchitectureismanagedbyNVIDIAsolutionsincludingNVIDIABaseCommand™,NVIDIAAIEnterprise,CUDA,andMagnumIO™.Thesetechnologieshelpkeepthesystemrunningatthehighestlevelsofavailability,performance,andwithNVIDIAEnterpriseSupport(NVEX),keepsallcomponentsandapplicationsrunningsmoothly.
Thisreferencearchitecture(RA)discussesthecomponentsthatdefinethescalableandmodulararchitectureoftheDGXSuperPOD.Thesystemisbuiltuponbuildingblocksofscalableunits(SU),eachcontaining32DGXH100systems,whichprovidesforrapiddeploymentofsystemsofmultiplesizes.ThisRAincludesdetailsregardingtheSUdesignandspecificsofInfiniBand,NVLinknetwork,Ethernetfabrictopologies,storagesystemspecifications,recommendedracklayouts,andwiringguides.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|ii
Contents
KeyComponentsoftheDGXSuperPOD 1
NVIDIADGXH100System 1
NVIDIAInfiniBandTechnology 2
RuntimeandSystemManagement 2
Components 3
DesignRequirements 4
SystemDesign 4
InfiniBandFabrics 4
ComputeFabric 4
StorageFabric 4
EthernetFabrics 5
In-BandManagementNetwork 5
Out-of-BandManagementNetwork 5
StorageRequirements 5
High-PerformanceStorage 5
UserStorage 5
DGXSuperPODArchitecture 6
NetworkFabrics 8
Compute—InfiniBandFabric 9
Storage—InfiniBandFabric 10
In-BandManagementNetwork 11
Out-of-BandManagementNetwork 12
StorageArchitecture 13
DGXSuperPODSoftware 16
NVIDIABaseCommand 16
NVIDIANGC 17
NVIDIAAIEnterprise 17
Summary 18
AppendixA.MajorComponents iii
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|1
KeyComponentsoftheDGXSuperPOD
TheDGXSuperPODarchitecturehasbeendesignedtomaximizeperformanceforstate-of-the-artmodeltraining,scaletoexaflopsofperformance,providethehighestperformancetostorageandsupportallcustomersintheenterprise,highereducation,research,andthepublicsector.ItisadigitaltwinofthemainNVIDIAresearchanddevelopmentsystem,meaningthecompany'ssoftware,applications,andsupportstructurearefirsttestedandvettedonthesamearchitecture.UsingSUs,systemdeploymenttimesarereducedfrommonthstoweeks.LeveragingtheDGXSuperPODdesignsreducestime-to-solutionandtime-to-marketofnextgenerationmodelsandapplications.
TheDGXSuperPODistheintegrationofkeyNVIDIAcomponents,aswellasstoragesolutionsfrompartnerscertifiedtoworkinaDGXSuperPODenvironment.
NVIDIADGXH100System
TheNVIDIADGXH100system
(Figure1
)isanAIpowerhousethatenablesenterprisestoexpandthefrontiersofbusinessinnovationandoptimization.TheDGXH100system,whichisthefourth-generationNVIDIADGXsystem,deliversAIexcellenceinaneightGPUconfiguration.TheNVIDIAHopperGPUarchitectureprovideslatesttechnologiessuchasthetransformerenginesandfourth-generationNVLinktechnologythatbringsmonthsofcomputationaleffortdowntodaysandhours,onsomeofthelargestAI/MLworkloads.
Figure1.DGXH100system
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|2
SomeofthekeyhighlightsoftheDGXH100systemovertheDGXA100systeminclude:>Upto9Xmoreperformancewith32petaFLOPSatFP8precision.
>Dual56-core4thGenIntel®Xeon®capableprocessorswithPCIe5.0supportandDDR5
memory.
>2Xfasternetworkingandstorage@400GbpsInfiniBand/EthernetwithNVIDIA
ConnectX®-7smartnetworkinterfacecards(SmartNICs).
>1.5XhigherbandwidthperGPU@900GBpswithfourthgenerationofNVIDIA
NVLink.
>640GBofaggregatedHBM3memorywith24TB/sofaggregatememorybandwidth,
1.5XhigherthanDGXA100system.
NVIDIAInfiniBandTechnology
InfiniBandisahigh-performance,lowlatency,RDMAcapablenetworkingtechnology,provenover20yearsintheharshestcomputeenvironmentstoprovidethebestinter-nodenetworkperformance.DrivenbytheInfiniBandTradeAssociation(IBTA),itcontinuestoevolveandleaddatacenternetworkperformance.
ThelatestgenerationInfiniBand,NDR,hasapeakspeedof400Gbpsperdirection.ItisbackwardscompatiblewiththepreviousgenerationsofInfiniBandspecifications.InfiniBandismorethanjustpeakperformance.InfiniBandprovidesadditionalfeaturestooptimizeperformanceincludingadaptiverouting(AR),collectivecommunicationwithSHARPTM,dynamicnetworkhealingwithSHIELDTM,andsupportsseveralnetworktopologiesincludingfat-tree,Dragonfly,andmulti-dimensionalTorustobuildthelargestfabricsandcomputesystemspossible.
RuntimeandSystemManagement
TheDGXSuperPODRArepresentsthebestpracticesforbuildinghigh-performancedatacenters.Thereisflexibilityinhowthesesystemscanbepresentedtocustomersandusers.NVIDIABaseCommandsoftwareisusedtomanageallDGXSuperPODdeployments.
DGXSuperPODcanbedeployedon-premises,meaningthecustomerownsandmanagesthehardwareasatraditionalsystem.Thiscanbewithinacustomer’sdatacenterorco-locatedatacommercialdatacenter,butthecustomerownsthehardware.Foron-premisessolutions,thecustomerhastheoptiontooperatethesystemwithasecure,cloud-nativeinterfacethroughNVIDIANGC™.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|3
Components
ThecomponentsoftheDGXSuperPODaredescribedin
Table1.
Table1.FourSU,127-nodeDGXSuperPODcomponents
Component
Technology
Description
Computenodes
127×NVIDIADGXH100systemwitheight80GBH100
GPUs
Fourthgenerationoftheworld’spremierpurpose-builtAIsystemsfeaturingNVIDIAH100TensorCoreGPUs,4thgenerationNVIDIANVLink®and3rdgenerationNVIDIANVSwitch™technologies.
Computefabric
NVIDIAQuantumQM9700NDR400GbpsInfiniBand
Rail-optimized,fullfat-treenetworkwitheightNDR400connectionspersystem
Storagefabric
NVIDIAQuantumQM9700NDR400Gb/sInfiniBand
Thefabricisoptimizedtomatchpeakperformanceoftheconfiguredstoragearray
Compute/storagefabricmanagement
NVIDIAUnifiedFabricManager,EnterpriseEdition
NVIDIAUFMcombinesenhanced,real-timenetworktelemetrywithAIpoweredcyberintelligenceandanalyticstomanagescale-outInfiniBanddatacenters
In-bandmanagement
network
NVIDIASN4600Cswitch
64port100GbpsEthernetswitchprovidinghighportdensitywithhighperformance
Out-of-band(OOB)managementnetwork
NVIDIASN2201switch
48port1GbpsEthernetswitchleveragingcopperportstominimizecomplexity
DGXSuperPODsoftwarestack
NVIDIABaseCommand
Manager
ClustermanagementforDGXSuperPOD
NVIDIAAIEnterprise
Best-in-classdevelopmenttoolsandframeworksfortheAIpractitionerandreliablemanagementandorchestrationforITprofessionals
MagnumIO
TheNVIDIAMAGNUMIOenablesincreasedperformanceforAIandHPC
NVIDIANGC
TheNGCcatalogprovidesacollectionofGPU-optimizedcontainersforAIandHPC
Userenvironment
Slurm
Slurmisaclassicworkloadmanagerusedtomanagecomplexworkloadsinamulti-node,batch-style,computeenvironment
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|4
DesignRequirements
TheDGXSuperPODisdesignedtominimizesystembottlenecksthroughoutthetightlycoupledconfigurationtoprovidethebestperformanceandapplicationscalability.Eachsubsystemhasbeenthoughtfullydesignedtomeetthisgoal.Inaddition,theoveralldesignremainsflexiblesothatdatacenterrequirementscanbetailoredtobetterintegrateintoexistingdatacenters.
SystemDesign
TheDGXSuperPODisoptimizedforacustomers’particularworkloadofmulti-nodeAI,HPC,andHybridapplications:
>AmodulararchitecturebasedonSUsof32DGXH100systemseach.
>AfullytestedsystemscalestofourSUs,butlargerdeploymentscanbebuiltbased
oncustomerrequirements.
>Rackdesigncansupportone,two,orfourDGXH100systemsperrack,sothatthe
racklayoutcanbemodifiedtoaccommodatedifferentdatacenterrequirements.
>StoragepartnerequipmentthathasbeencertifiedtoworkinDGXSuperPOD
environments.
>Fullsystemsupport(includingcompute,storage,network,andsoftware)isprovided
byNVIDIAEnterpriseSupportNVES).
InfiniBandFabrics
ComputeFabric
>TheInfiniBandcomputefabricisrail-optimizedtothetoplayerofthefabric.>TheInfiniBandfabricisabalanced,full-fattree.
>ManagedNDRswitchesareusedthroughoutthedesigntoprovidebetter
managementofthefabric.
>ThefabricisdesignedtosupportthelatestSHaRPv3features.
StorageFabric
Thestoragefabricprovideshighbandwidthtosharedstorage.Italsohasthesecharacteristics:
>Itisindependentofthecomputefabrictomaximizeperformanceofbothstorage
andapplicationperformance.
>Providessingle-nodebandwidthofatleast40GBpstoeachDGXH100system.>StorageisprovidedoverInfiniBandandleveragesRDMAtoprovidemaximum
performanceandminimizeCPUoverhead.
>Itisflexibleandcanscaledtomeetspecificcapacityandbandwidthrequirements.>User-accessiblemanagementnodesprovideaccesstosharedstorage.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|5
EthernetFabrics
MultipleEthernetfabricsareusedtosupportmanagementcommunications,Ethernet-basedstoragetargets,Internetaccess,andothertraditionalTCP/IPbasedservices.
In-BandManagementNetwork
>Thein-bandmanagementnetworkfabricisEthernet-basedandisusedfornode
provisioning,datamovement,Internetaccess,andotherservicesthatmustbeaccessiblebytheusers.
>Thein-bandmanagementnetworkconnectionsforcomputeandmanagement
serversoperateat100Gbpsandarebondedforresiliency.
Out-of-BandManagementNetwork
TheOOBmanagementnetworkconnectsallthebasemanagementcontroller(BMC)ports,aswellasotherdevicesthatshouldbephysicallyisolatedfromsystemusers.
StorageRequirements
TheDGXSuperPODcomputearchitecturemustbepairedwithahigh-performance,balanced,storagesystemtomaximizeoverallsystemperformance.TheDGXSuperPODisdesignedtousetwoseparatestoragesystems,high-performancestorage(HPS)anduserstorage,optimizedforkeyoperationsofthroughput,parallelI/O,aswellashigherIOPSandmetadataworkloads.
High-PerformanceStorage
HPSmustprovide:
>High-performance,resilient,POSIX-stylefilesystemoptimizedformulti-threadedreadandwriteoperationsacrossmultiplenodes.
>NativeInfiniBandsupport.
>LocalsystemRAMfortransparentcachingofdata.
>Leveragelocaldisktransparentlyforcachingoflargerdatasets.
UserStorage
Userstoragemust:
>Bedesignedforhighmetadataperformance,IOPS,andkeyenterprisefeaturessuch
ascheckpointing.ThisisdifferentthantheHPS,whichisoptimizedforparallelI/Oandlargecapacity.
>CommunicateoverEthernettoprovideasecondarypathtostorageso,thatinthe
eventofafailureofthestoragefabricorHPS,nodescanstillbeaccessedandmanagedbyadministratorsinparallel.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|6
DGXSuperPODArchitecture
TheDGXSuperPODarchitectureisacombinationofDGXsystems,InfiniBandandEthernetnetworking,managementnodes,andstorage.
Figure2
showstheracklayoutofasingleSU.Inthisexample,powerconsumptionperrackexceeds40kW.Theracklayoutcanbeadjustedtomeetlocaldatacenterrequirements,suchasmaximumpowerperrackandracklayoutbetweenDGXsystemsandsupportingequipmenttomeetlocalneedsforpowerandcoolingdistribution.
Figure2.CompletesingleSUracklayout
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|7
Figure3
showsatypicalmanagementrackconfigurationwithInfiniBandandEthernet
switches,managementservers,storagearrays,andUFMappliances.
Figure3.Typicalmanagementrack
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|8
NetworkFabrics
SeveralnetworksaredeployedontheDGXSuperPOD.Thecomputefabricisusedforinter-nodecommunicationthroughtheapplications.Aseparatestoragefabricisusedtoisolatestoragetraffic.TherearetwoEthernetfabricsforin-bandandOOBmanagement.Requirementsforeachsectionaredetailedbelow.Inaddition,designsforthenetworkareprovidedaftertherequirements.
Figure4
showsthedifferentportsonthebackoftheDGXH100CPUtrayandtheconnectivityprovided.TheInfiniBandcomputefabricportsinthemiddleuseatwo-porttransceivertoaccessalleightGPUs.Eachpairofin-bandEthernetmanagementandInfiniBandstorageportsprovideparallelpathwaysintotheDGXH100systemforincreasedperformance.TheOOBportisusedforBMCaccess.Inaddition,thereisanadditionalLANportnexttotheBMCbutisnotusedintheDGXSuperPOD.
Figure4.DGXH100networkports
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|9
Compute—InfiniBandFabric
Figure5
showsthecomputefabriclayoutforthefull127-nodeDGXSuperPOD.Eachgroupof32nodesisrail-aligned.TrafficperrailoftheDGXH100systemsisalwaysonehopawayfromtheother31nodesinaSU.Trafficbetweennodes,orbetweenrails,traversesthespinelayer.
Figure5.ComputeInfiniBandfabricforfull127nodeDGXSuperPOD
Table2
showsthenumberofcablesandswitchesrequiredforthecomputefabricfordifferentSUsizes.
Table2.Computefabriccomponentcount
SUCount
Cluster
Size#
Nodes
ClusterSize
#GPUs
LeafSwitchCount
SpineSwitchCount
Compute+UFM
NodeCable
Count
Spine-LeafCableCount
1
311
248
8
4
252
256
2
63
504
16
8
508
512
3
95
760
24
16
764
768
4
127
1016
32
16
1020
1024
1.Thisisa32nodeperSUdesign,howeveraDGXNodemustberemovedtoaccommodateforUFMconnectivity.
BuildingsystemsbySUprovidesthemostefficientdesigns.However,ifadifferentnodecountisrequiredduetobudgetaryconstraints,datacenterconstraints,orotherneeds,thefabricshouldbedesignedtosupportthefullSU,includingleafswitchesandleaf-spinecables,andleavetheportionofthefabricunusedwherethesenodeswouldbelocated.Thiswillensureoptimaltrafficroutingandensurethatperformanceisconsistentacrossallportionsofthefabric.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|10
Storage—InfiniBandFabric
ThestoragefabricemploysanInfiniBandnetworkfabricthatisessentialtomaximumbandwidth
(Figure6
).ThisisbecausetheI/Oper-nodefortheDGXSuperPODmustexceed40GBps.High-bandwidthrequirementswithadvancedfabricmanagementfeatures,suchascongestioncontrolandAR,providesignificantbenefitsforthestoragefabric.
Figure6.InfiniBandstoragefabriclogicaldesign
Thestoragefabricuses
MQM9700-NS2F
switches
(Figure7
).Thestoragedevicesareconnectedata1:1porttouplinkratio.TheDGXH100systemconnectionsareslightlyoversubscribedwitharationear4:3withadjustmentsasneededtoallowformorestorageflexibilityregardingcostandperformance.
Figure7.MQM9700-NS2Fswitch
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|11
In-BandManagementNetwork
Thein-bandmanagementnetworkprovidesseveralkeyfunctions:
>Connectsalltheservicesthatmanagethecluster.
>Enablesaccesstothehomefilesystemandstoragepool.
>Providesconnectivityforthein-clusterservicessuchasBaseCommandManager,
SlurmandtootherservicesoutsideoftheclustersuchastheNGCregistry,coderepositories,anddatasources.
Figure8
showsthelogicallayoutofthein-bandEthernetnetwork.Thein-bandnetworkconnectsthecomputenodesandmanagementnodes.Inaddition,theOOBnetworkisconnectedtothein-bandnetworktoprovidehigh-speedinterfacesfromthemanagementnodestosupportparalleloperationstodevicesconnectedtotheOOBstoragefabric,suchasstorage.
Figure8.In-bandEthernetnetwork
Thein-bandmanagementnetworkuses
SN4600C
switches
(Figure9
).
Figure9.SN4600Cswitch
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|12
Out-of-BandManagementNetwork
Figure10
showstheOOBEthernetfabric.ItconnectsthemanagementportsofalldevicesincludingDGXandmanagementservers,storage,networkinggear,rackPDUs,andallotherdevices.Theseareseparateontotheirownfabricsincethereisnouse-casewhereusersneedaccesstotheseportsandaresecuredusinglogicalnetworkseparation.
Figure10.LogicalOOBmanagementnetworklayout
TheOOBmanagementnetworkusesSN2201switches
(Figure11
).
Figure11.SN2201switch
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|13
StorageArchitecture
Data,lotsofdata,isthekeytodevelopmentofaccuratedeeplearning(DL)models.Datavolumecontinuestogrowexponentially,anddatausedtotrainindividualmodelscontinuestogrowaswell.Dataformat,notjustvolumecanplayakeyfactorintherateatwhichdataisaccessed.TheperformanceoftheDGXH100systemisuptoninetimesfasterthanitspredecessor.Toachievethisinpractice,storagesystemperformancemustscalecommensurately.
ThekeyI/OoperationinDLtrainingisre-read.Itisnotjustthatdataisread,butitmustbereusedagainandagainduetotheiterativenatureofDLtraining.Purereadperformancestillisimportantassomemodeltypescantraininafractionofanepoch(ex:somerecommendermodels)andinferenceofexistingcanbehighlyI/Ointensive,muchmoresothantraining.Writeperformancecanalsobeimportant.AsDLmodelsgrowinsizeandtime-to-train,writingcheckpointsisnecessaryforfaulttolerance.ThesizeofcheckpointfilescanbeterabytesinsizeandwhilenotwrittenfrequentlyaretypicallywrittensynchronouslythatblocksforwardprogressofDLmodels.
Ideally,dataiscachedduringthefirstreadofthedataset,sodatadoesnothavetoberetrievedacrossthenetwork.SharedfilesystemstypicallyuseRAMasthefirstlayerofcache.Readingfilesfromcachecanbeanorderofmagnitudefasterthanfromremotestorage.Inaddition,theDGXH100systemprovideslocalNVMestoragethatcanalsobeusedforcachingorstagingdata.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|14
DGXSuperPODisdesignedtosupportallworkloads,butthestorageperformancerequiredtomaximizetrainingperformancecanvarydependingonthetypeofmodelanddataset.Theguidelinesin
Table3
and
Table4
areprovidedtohelpdeterminetheI/Olevelsrequiredfordifferenttypesofmodels.
Table3.Storageperformancerequirements
PerformanceLevel
WorkDescription
DatasetSize
Good
NaturalLanguageProcessing(NLP)
Datasetsgenerallyfitwithinlocalcache
Better
Imageprocessingwithcompressedimages(ex:ImageNet)
Manytomostdatasetscanfitwithinthelocalsystem’scache
Best
Trainingwith1080p,4K,or
uncompressedimages,offline
inference,ETL,
Datasetsaretoolargetofitintocache,massivefirstepochI/Orequirements,workflowsthatonlyreadthedatasetonce
Table4.Guidelinesforstorageperformance
PerformanceCharacteristic
Good(GBps)
Better(GBps)
Best(GBps)
Single-noderead
4
8
40
Single-nodewrite
2
4
20
SingleSUaggregatesystemread
15
40
125
SingleSUaggregatesystemwrite
7
20
62
4SUaggregatesystemread
60
160
500
4SUaggregatesystemwrite
30
80
250
Evenforthebestcategoryabove,itisdesirablethatthesinglenodereadperformanceisclosertothemaximumnetworkperformanceof80GBps.
Note:Asdatasetsgetlarger,theymaynolongerfitincacheonthelocalsystem.PairinglargedatasetsthatdonotfitincachewithveryfastGPUscancreateasituationwhereitisdifficulttoachievemaximumtrainingperformance.NVIDIAGPUDirectStorage®(GDS)providesawaytoreaddatafromtheremotefilesystemorlocalNVMedirectlyintoGPUmemoryprovidinghighersustainedI/Operformancewithlowerlatency.UsingthestoragefabricontheDGXSuperPOD,aGDS-enabledapplicationshouldbeabletoreaddataatover40GBpsdirectlyintotheGPUs.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|15
High-speedstorageprovidesasharedviewofanorganization’sdatatoallnodes.Itmustbeoptimizedforsmall,randomI/Opatterns,andprovidehighpeaknodeperformanceandhighaggregatefilesystemperformancetomeetthevarietyofworkloadsanorganizationmayencounter.High-speedstorageshouldsupportbothefficientmulti-threadedreadsandwritesfromasinglesystem,butmostDLworkloadswillberead-dominant.
Usecasesinautomotiveandothercomputervision-relatedtasks,where1080pimagesareusedfortraining(andinsomecasesareuncompressed)involvedatasetsthateasilyexceed30TBinsize.Inthesecases,4GBpsperGPUforreadperformanceisneeded.
WhileNLPcasesoftendonotrequireasmuchreadperformancefortraining,peakperformanceforreadsandwritesareneededforcreatingandreadingcheckpointfiles.Thisisasynchronousoperationandtrainingstopsduringthisphase.Ifyouarelookingforbestend-to-endtrainingperformance,donotignoreI/Ooperationsforcheckpoints.
Theprecedingmetricsassumeavarietyofworkloads,datasets,andneedfortraininglocallyanddirectlyfromthehigh-speedstoragesystem.Itisbesttocharacterizeworkloadsandorganizationalneedsbeforefinalizingperformanceandcapacityrequirements.
NVIDIADGXSuperPOD:NextGenerationScalableInfrastructureforAILeadershipRA-11333-001v6|16
DGXSuperPODSoftware
DGXSuperPODisanintegratedhardwareandsoftwaresolution.Theincludedsoftware
(Figure12
)isoptimizedforAIfromtoptobottom,fromtheacceleratedframeworksandworkflowmanagementthroughtosystemmanagementandlow-leveloperatingsystem(OS)optimizations,everypartofthestackisdesignedtomaximizetheperformanceandvalueofDGXSuperPOD.
Figur
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