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AsurveyonPerformanceAnalyticalTools
forPartitionedGlobalAddressSpace
2019‑11‑08
TakuyaFukuoka,TauraLaboratory
1
Introduction
WiththeendofMoore'sLaw,CPU'sclockfrequencystopped
gettingfaster
Thenecessitytoutilizemachineswhichhavemanynodeseachof
whichhavemanycores
Oakbridge‑CX:1268nodes,56cores
Parallelismisimportantforhighperformancecomputing
Weneedsoftware(language,library)tosupportparallelism
    
fromhttps://www.cc.u‑tokyo.ac.jp/supercomputer/obcx/service 2
DistributedMemoryandSharedMemory
Betweennodes,thememoryisdistributed
Weneedinter‑nodecommunicationbyMPI(Message
PassingInterface)
MPIisnotoriousforitspoorprogramability
Betweencoresinonenode,memoryisshared
WecalculatewithsharedmemorylibrariessuchasOpenMP
MPI+OpenMParestandardinparallelcomputing 3
SoftwareDistributedSharedMemory
(SoftwareDSM)
Programmerscanhandledistributedmemoryasifitwereshared
memory
Noneedtodescribeinter‑nodecommunication
Theproblemisthedifficultyindetectingthecauseofperformance
degradation
High‑levelabstractionandunderlyingsoftwarelayersobscure
memoryaccesspatterns
Communicationisconductedimplicitly
PerformanceanalyticaltoolsforDSMarenecessary(myreserach
theme) 4
PerformanceAnalyticalTools(PATs)
Theyarebasedonmeasure‑modifycycle
Datacollection
Dataanalysis
Datavisualizaion
Optimization
ManytoolshavebeendevelopedsuchasHPCToolkit[1],Tau[2]
Tomyknowledge,noPATsspecializedforsoftwareDSMexists
[1]Adhianto,L.,Banerjee,S.,Fagan,M.,Krentel,M.,Marin,G.,Mellor‑Crummey,J.,&Tallent,N.R.(2009).
HPCTOOLKIT:toolsforperformanceanalysisofoptimizedparallelprograms.ConcurrencyandComputation:
PracticeandExperience,22(6)https://doi.org/10.1002/cpe.1553
[2]Shende,S.S.,&Malony,A.D.(2006).TheTauParallelPerformanceSystem.InTheInternationalJournalof
HighPerformanceComputingApplications(Vol.20,pp.287–311).
5
PGAS(PartitionedGlobalAddressSpace)
Intermsofprogrammabiltyandperformance,PGASislocated
halfwaybetweenMPI+OpenMPandDSM
PGASprogrammingmodelsintroducetwokindsofmemory
(global)sharedmemoryandlocalmemory
Inthispresentation,Iintroduceasurveyonperformanceanalytical
toolsforPGASsystems
ManycommonpointswithDSMsuchashigh‑level
abstractionandimplicitcommunication 6
Outline
PGAS(PartitionedGlobalAddressSpace)
PerformanceAnalyticalToolsforPGAS
PerformanceParallelWizard
ChplBlamer
Conclusion
7
PGASmemorymodel
Memoryspaceislogicallydividedintotwoparts
Aglobalportionpartitionedamongthenodes(globalshared
memory)
Aprivateportionlocaltoeachnode(localmemory)
Asharedmemoryspacehasaffinitytoaparticularnode
Localaccessistypicallymuchfasterthanremoteaccess
Whenthedataisremote,itcanbefetchedbyone‑side
communication 8
PGASfamily
ManytypesofPGASsystemswithvariousinterfaceshavebeen
developed
UPC(UnifiedParallelC)[1]
Clanguageextension
SHMEM
Designedtoexposelow‑levelhardwarecapabilitieswith
minimaloverhead
Chapel[2]
Aparallelprogramminglanguagetosupporttaskparallelism
GlobalAarray,Co‑Array‑Fortranandetc......
[1]El‑Ghazawi,T.,&Cantonnet,F.(2002).UPCPerformanceandPotential:ANPBExperimentalStudy.InSC’02
Proceedingsofthe2002ACM/IEEEconferenceonSupercomputing(pp.1–26).
https://doi.org/10.1109/sc.2002.10034
[2]Chamberlain,B.L.,Callahan,D.,&Zima,H.P.(2007).ParallelProgrammabilityandtheChapelLanguage.The
InternationalJournalofHighPerformanceComputingApplications,21(3),291–312.
https://doi.org/10.1177/1094342007078442
9
ProgramabilityofPGASandMPI
TheleftfigureispseudocodeofChapelandtherightisoneofMPI
fordatathree‑pointstencil
ChapelcodesissimplerthanMPIcodes
Chamberlain,B.L.,Callahan,D.,&Zima,H.P.(2007).ParallelProgrammabilityandtheChapelLanguage.The
InternationalJournalofHighPerformanceComputingApplications,21(3),291–312.
https://doi.org/10.1177/1094342007078442 10
UPCMatrix‑VectorMultiplication(1)
#include <upc_relaxed.h>
shared int a[THREADS][THREADS] ;
shared int b[THREADS], c[THREADS] ;
void main (void)
{
int i, j;
upc_forall( i = 0 ; i < THREADS ; i++; i){
c[i] = 0;
for ( j= 0 ; j < THREADS ; j++)
c[i] += a[i][j]*b[j];
}
}
fromhttp://www.training.prace‑ri.eu/training_material/uploads/tx_pracetmo/UPC_Edinburgh30March2011.pdf 11
UPCMatrix‑VectorMultiplication(2)
#include <upc_relaxed.h>
shared [THREAD] int a[THREADS][THREADS] ;
shared int b[THREADS], c[THREADS] ;
void main (void)
{
int i, j;
upc_forall( i = 0 ; i < THREADS ; i++; i){
c[i] = 0;
for ( j= 0 ; j < THREADS ; j++)
c[i] += a[i][j]*b[j];
}
}
fromhttp://www.training.prace‑ri.eu/training_material/uploads/tx_pracetmo/UPC_Edinburgh30March2011.pdf 12
Outline
PGAS(PartitionedGlobalAddressSpace)
PerformanceAnalyticalToolsforPGAS
PerformanceParallelWizard
ChplBlamer
Conclusion
13
PerformanceParallelWizard(PPW)[1]
AperformaceanalysissystemforPGASandMPIsystem
UPC,SHMEM,MPI
Developedtofacilitatesupportformultipleprogrammingmodels
EachPGASsystemhasitsspecificinterface
Itcanbeeasilyadoptedtosupportmultiplemodels
Twonewconcepts
GASP‑enableddatacollections
generic‑operation‑typeabstraction
[1]Su,H.H.,Billingsley,M.,&George,A.D.(2010).Parallelperformancewizard:Aperformancesystemforthe
analysisofpartitionedglobal‑address‑spaceapplications.InternationalJournalofHighPerformanceComputing
Applications,24(4),485–510.https://doi.org/10.1177/1094342010370953
14
PPWOverview
15
DataCollection
Auser'ssourceprogramisfirstcompiledusingPPW'scommands
togenerateaninstrumentedexecutable
Shifttheinstrumentationresponsibilityfromtoolwritertocompiler
writer
GASP(Global‑Address‑SpacePerformance)interfacespecifies
therelationsheepbetweenprogrammingmodelimplementation
andperformanceanalysistools
16
Generic‑operation‑typeAbstraction
Itmapsmodel‑specificconstructstoasetofmodel‑independent
genericoperationtypesclassifiedbytheirfunctionalities
Forexample,upc_memput()andshmem_put()areclassifiedinto
oneofgenericoperationtype:"Explicitput"
Itcanavoidre‑implementingthesamefunctionalityforeachmodel
17
Manual/AutomaticAnalysis
ManualAnalysis
ConvertingtracingdatatoSLOG2orOTFformat
Viewingwithexistingtools
Jumpshot,Vampirandetc...
Beingeasytounderstandhowtousevisualizers
AutomaticAnalysis
Asthesizeoftheperformancedatasetgrows,itbecomesnearly
impossiblefortheusertomanuallyexaminethedataandfind
performaceissues
VisualizationissuppliedbyPPW
18
Visualization
Manykindsofvisualizationareprovided
Avisualizationtoshowload‑balancingissues(leftfigure)
Acharttocomparerelatedexperimentalruns(middlefigure)
AuniqueUPC‑specificarraydistributiondiisplay(rightfigure)
etc......
ScreenshotsarefromapaperofPPW,whichIreferencedbefore
19
PPWOverheadEvaluation
Thedatacollectionoverheadnumbersarecomparabletoexisting
performancetools(<2.7%forprofile,<4.3%fortrace)
1‑5%underprofilemodeand1‑10%undertracemodeis
consideredtobesafe
20
Outline
PGAS(PartitionedGlobalAddressSpace)
PerformanceAnalyticalToolsforPGAS
PerformanceParallelWizard
ChplBlamer
Conclusion
21
ChplBlamer[1]
Adata‑centricandcode‑centriccombinedprofilerforChapel
program
InPGASsystems,theonlyuseofcode‑centricapproachdoesnot
effective
Memoryallocationanddatamovementareoftenthekey
bottleneck
Fordata‑centricapproach,thissystemadoptsaconceptof
"blame"
ChplBlamerleveragesstaticanalysisanddynamicanalysistofind
performancebottlenecks
[1]Zhang,H.,&Hollingsworth,J.K.(2018).ChplBlamer:AData‑centricandCode‑centricCombinedProfilerfor
Multi‑localeChapelPrograms.InProceedingsoftheInternationalConferenceonSupercomputing(pp.252–262).
NewYork,NewYork,USA:ACMPress.https://doi.org/10.1145/3205289.3205314
22
Code‑centricvsData‑centric(1)
fromhttp://ics2018.ict.ac.cn/slides/ICS'18‑Hui‑Slides.pdf
23
Code‑centricvsData‑centric(2)
Code‑centricapproach(traditional)
Helpfulinpinpointinghotspotsinprogramsatthegranularity
ofproceduresorstatement
Data‑centricapproach
Itassociatesperformancelosseswithdifferentvariables
accessedbyspecificlinesofthecode
Youcangainpotentialspeedupsbyoptimizingtheaccess
patternordatalayoutoftheproblematicvariables
24
HowtoCalculateBlame?
Foreachvariablev,therearethreeconceptsintheblame
calculation
BlameSet
Asetofstatementsthataffectthevalueofv.
BlameSamples
Asetofsamplesattributedtov
Blame
Theproportionofthesizeofv'sBlameSamplesoverthe
totalnumberofsamplescollected
25
AnExampleofBlameCalculation
26
AnExampleofBlameCalculation
27
AnExampleofBlameCalculation
28
AnExampleofBlameCalculation
29
ChplBlamerToolFramework
ChplBlamerleveragesstaticanalysisanddynamicanalysis
Itiscomposedof4phases
30
1.Intra‑proceduralStaticAnalysis
Intra‑proceduralanalysisforblamecalculation
Graphsareusedtorepresentintra‑proceduralblame
relationshipsbetweenvariables
Eachvariables:avertex
Eachdatadependencyrelationship:adirectededge
AnExample:"storeatob",thenb‑>a
31
2.MonitoredExecution
UtilizethePAPIlibraryastheinterfacetoleveragethehardware
counters
fromhttp://icl.utk.edu/projects/papi/wiki/PAPIC:Overview#Architecture
32
3.PostProcessing
Atthispoints,youhavebothstaticandruntimeinformationofa
Chapelprogram
Inter‑proceduralanalysis
Propagationofblameisconductedthrough"exitvariables"
thathaveascopeoutoffunctions
Globalvariables,incomingparametersthatarepointers,
andreturnvalues
Calculatetheblamedvariablesforeachsamplecollectedduring
theexecution
33
4.GUIPresentation
SampleProfilesareaggregated
Eachuser‑definedvariableispresentedviaaGUI
4kindsofviews
Fulldatacentric,blamepoint(data/codehybrid),fullcode
centric,loadbalancing
Blamepointview:stopblamepropagationatacertain
pointinthecall 34
CaseStudyofChplBlamer
32Node
Samplingperiod:1073807359
3benchmarksofchapelapplications
HPL:1.05xspeedups
ISx:1.11xspeedups
LULESH:4.0xspeedups
35
Outline
PGAS(PartitionedGlobalAddressSpace)
PerformanceAnalyticalToolsforPGAS
PerformanceParallelWizard
ChplBlamer
Conclusion
36
Conclusion
Performanceanalyticaltools(PATs)specializedforDSMand
PGASisimportantbecauseofitshigh‑levelabstractionandimplicit
communication
ImainlyintroducedtwokindsofPATsforPGAS,Performance
ParallelWizard(PPW)andChplBlamer.
PPWisdevelopedtofacilitatesupportformultipleprogramming
modelswithtwoconceptof"GASP‑enableddatacollections"and
"generic‑operation‑typeabstraction"
ChplBlameradoptdata‑centricapproachwithaconceptof
"blame"
WhilecommunicationisimportantinanalysisofbothDSMand
PGAS,additionalprofileforcachingisalsoneededforDSM
research 37
38
AnExampleofGASPInterface
gasp_event_notify()
ThemostimportantentrypointintheGASPinterface
Eventcallbackfunctionthatcompilersuses
Tonotifyeventsofpotentialinterestoccuratruntime
Toprovideusefulinformationtotheperformanceanalysis
tools
39
OtherPerformanceAnalyticalToolsfor
Chapel
Purity[1]
Itanalyzesmemoryandcommunicationaccesspatternsover
multi‑localeChapelprograms
Chplvis[2]
Itvisualizestheinter‑localecommunicationandtask
computationofChapelprograms
Ithelpstheuserstodiscoverthepitfallsofcertainusesof
parallelism
[1]Johnson,R.B.,&Hollingsworth,J.J.(2018).Purity:AnIntegrated,Fine‑Grain,Data‑Centric,Communication
ProfilerfortheChapelLanguage.In2018IEEEInternationalParallelandDistributedProcessingSymposium
Workshops(IPDPSW)(pp.934–942).IEEE.https://doi.org/10.1109/IPDPSW.2018.00147
[2]Nelson,P.A.,&Titus,G.(2016).Chplvis:ACommunicationandTaskVisualizationToolforChapel.In2016
IEEEInternationalParallelandDistributedProcessingSymposiumWorkshops(IPDPSW)(pp.1578–1585).IEEE.
https://doi.org/10.1109/IPDPSW.2016.46
40
OtherPerformanceAnalyticalToolsfor
PGAS
forX10[1]
Itvisualizeimplicitdatatransferamongplacesand
synchronizationamongactivities
forGASNet[2]
ItextendsPPWforGASNet,alow‑levelcommunicationlibrary
underPGASmodelssuchasUPCandSHMEM
[1]Itahashi,S.(2014).TowardaprofilingtoolforvisualizingimplicitbehaviorinX10.In2014X10Workshop
(X10’14)(pp.1–5).X10Workshop2014.
[2]Prakash,P.,III,M.B.,George,A.,&Aggarwal,V.(2011).PerformanceAnalysisFrameworkforGASNet
Middleware,Tools,andApplications.InPGAS’11.
41

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