areprovidedtoviewprograminformationgatheredbythecompilerandrelateittoinformation

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1 ParallelProgrammingandPerformanceEvaluationwithThe InsungParkMichaelVossBrianArmstrongRudolfEigenmann SchoolofElectricalandComputerEngineering UrsaToolFamily andtheirintegrationwithperformanceevaluationenvironments.first,weproposeinteractivecompilationscenariosinsteadoftheusualblack-box-orienteduseofcompilertools.insuchscenarios, informationgatheredbythecompilerandthecompiler'sreasoningarepresentedtotheuserinmeaningfulwaysandon-demand.second,atightintegrationofcompilationandperformanceanalysis toolsisadvocated.manyoftheexisting,advancedinstrumentsforgatheringperformanceresults arebeingusedinthepresentedenvironmentandtheirresultsarecombinedinintegratedviews withcompilerinformationanddatafromothertools.initialinstrumentsthatassistusersin\data Abstract Thispapercontributestothesolutionofseveralopenproblemswithparallelprogrammingtools PurdueUniversity toolbymakingavailablethegatheredresultstotheusercommunityatlargeviatheworld-wide Web. usersataspecicsite,suchasaresearchordevelopmentproject.ursamajorcomplementsthis mining"thisinformationarepresentedandtheneedformuchstrongerfacilitiesisexplained. toolfamily.twocasestudiesarepresentedthatillustratetheuseofthetoolsfordevelopingand studyingparallelapplicationsandforevaluatingparallelizingcompilers. Thispaperpresentsobjectives,functionality,experience,andnextdevelopmentstepsoftheUrsa TheUrsaFamilyprovidestwotoolsaddressingtheseissues.UrsaMinorsupportsagroupof 1Introduction occur.inothercases,usersmayknowthatthearraysectionsaccessedindierentloopiterationsdonot theseshortcomings.forexample,althoughthecompilerdetectsavalue-specicdatadependence,the tool.onedisadvantageofthisscenarioisthatthecompilermayhaveinsucientknowledgeorlimited usermayknowthatineveryreasonableprograminputthevaluesaresuchthatthedependencedoesnot capabilitiestoparallelizeaprogramoptimally.insomecasesitwouldbeeasyfortheusertomakeupfor compileristhattheconversionofagivenserialprogramintoparallelformisdonemechanicallybythe importantclassofsuchtools[bde+96,haa+96].theapparentadvantageofusingaparallelizing thechallengingtaskofdevelopingwell-performingparallelprograms.parallelizingcompilersareone Interactiveuseofparallelizingcompilers.Manyprogrammingtoolsexistthatassisttheuserin overlap.furthermore,certainprogramtransformationsmaymakeasubstantialperformancedierence, ndthereasonwhyaloopwasnotparallelizedautomatically,asmallmodicationmaybeappliedthat butareapplicabletoveryfewprograms,andhencenotbuiltintoacompiler'srepertoire.ifausercan ensuresparallelexecution.becauseofthesereasons,manualcodemodicationinadditiontoautomatic parallelizationisoftennecessarytoachievegoodperformance. REERaward.ThisworkisnotnecessarilyrepresentativeofthepositionsorpoliciesoftheU.S.ArmyortheGovernment. ThisworkwassupportedinpartbyPurdueUniversity,U.S.Armycontract#DABT63-92-C-33,andanNSFCA- 1

2 timinginformationbecomesavailablefromvariousprogramruns,structuralinformationoftheprogram Integratedcompilationandperformanceevaluation.Duringtheprocessofcompilingaparallel formation.findingparallelismstartsfromlookingthroughthisinformationandlocatingpotentially programandmeasuringitsperformance,aconsiderableamountofinformationisgathered.forexample, isgatheredfromthecodedocumentation,andcompilersoeralargeamountofprogramanalysisin- accompanyingthisprocedureisoftenoverwhelming.toolsthatassistthisprocessareimportant. ofthecompilationprocess,thecharacteristicsofthegivenprogram,itsperformanceresults,andthe parallelsectionsofcode.improvingparallelperformanceistheimmediatenextstep.decisionsare relationshipsofthesedata.itisthebasisforenhancingtheperformanceofanexistingparallelprogram madebasedontimingresultsandtheirrelationshiptoprogramcharacteristics.thebookkeepingeort aswellasforbeginningtoparallelizeaserialprogram. gathersinformationalongthecourseofcompilingandrunningaprogramandpresentsitinaformat parallelization.thetoolhelpsaprogrammerunderstandthestructureofaprogram,identifyparallelism, andcompareperformanceresultsofdierentprogramvariants.thetool,ursaminor[pvae97], thatiseasytolookupandcomprehend.usingthetool,theprogrammercomestoanunderstanding ThepresentedtooliscloselyrelatedtothePolariscompilerinfrastructure[BDE+96].Polaris,asa Inthispaper,weintroduceanon-goingtoolprojectthatsupportsascenarioofuser-plus-compiler symbolicprogramanalysis.polarisalsorepresentsageneralinfrastructureforanalyzingandmanipulatingfortranprograms,whichcanprovideusefulinformationabouttheprogramstructureandits potentialparallelism.polarisplaysamajorroleingeneratingthedatalesusedasinputtoursa parallelismdetection. parallelapplications.section5thenshowstwocasestudiesofursaminorinuse.section6concludes discussesitsfunctionality.sectionpresentstheursamajortool[pe98],aweb-basedtoolbuiltupon UrsaMinorthatwasdesignedfordistributionandevaluationofexperimentalresultswithvarious thepaper. 2ObjectivesofUrsaMinor Section2presentsourobjectivesindevelopingUrsaMinor.Section3givesanoverviewand compiler,includesadvancedprogramanalysisandtransformationtechniques,suchasarrayprivatization,symbolicandnonlineardatadependencetesting,idiomrecognition,interproceduralanalysis,and Minor.Examplesofsuchlesareloopparallelizationsummaries,data-dependenceinformation,and loop/subroutinecallgraphs.polarisalsoinstrumentsprogramsfortimingmeasurementsandmaximum exploitingparallelism,thetoolpursuesthefollowingobjectives: IntegratedBrowsersforProgram,Compilation,andPerformanceData:TheUrsaMinor TheintendedusersoftheUrsaMinortoolareparallelprogrammersthathavesomeexperienceusingparallelizingcompilersandperformanceanalysistools.Inordertoassisttheminidentifyingand InteractiveCompilers:Thecurrent,predominantlyblack-boxuseofparallelizingcompilersneeds detailswheneverheorshefeelstheneedtoconcentrateonaspecicportionoftheprogram.the ofaprogram.inthisway,ausercanstartfromanoverallviewoftheprogramandinspectthe toolcollectsandfacilitatestheuseofprogram,compilation,andperformancedata.theinformationneedstobepresentedinaformatthatconveyshigh-levelaswellasdetaileddescriptions [MCC+95],andPTOPP[EM93]performanceanalysisenvironments. toolcomplementsandintegratescapabilitiesprovidedbytoolssuchasthepablo[ree9],paradyn tobechangedintoaninteractivescenario.thisgoesbeyondinteractivepassinvocationaspioneeredbytoolssuchasstart/pat[asm89]andparascope[bkk+89].theultimategoalofthe UrsaMinorprojectistoprovideacomprehensiveenvironmentthatencompassestheprocessof writing,compiling,running,andimprovingparallelprograms.tothisendinteractivecapabilities 2

3 performancedata,andvisualizingthisinformation.theursaminorenvironmentprovidesaidsforthe dynprojects,whichprovideadvancedfacilitiesforoptimizingandinstrumentingprograms,gathering usertounderstandthegatheredperformancedataandtoreasonabouttheinformationinaninteractive similarobjectivetothatofvtune[int97],whichisanadvancedtoolforsingle-processorsystems. way.inthesensethatthetoolprovidesuserswithadvicetoimproveperformance,ursaminorhasa Theseobjectivesdistinguishourapproachfromrelatedeorts,suchasthePolaris,PabloandPara- areprovidedtoviewprograminformationgatheredbythecompilerandrelateittoinformation Inadditiontothemainobjectives,weobservethefollowingdesignrulestomakeourtoolmoreuseful providedbyotherprogrammingtools. andeasilyaccessible: Portability:Fordisseminatinganewtooltotheusercommunity,itisimportantthatitbeeasyto compileranditsperformanceanalysislibraries,whichthemselvesareportabletomanyplatforms. independentjavalanguage,andbyusingonlywidely-availableapplicationprogramminginter- faces(apis).thetoolmakesuseofinformationgatheredbyotherfacilities,suchasthepolaris Inaddition,UrsaMinoristobeexibleinthedataformatitcanread,suchthatitcanadapt installonnewplatforms.weapproachthisgoalbyimplementingursaminorinthetarget- Expandability:ThemainfunctionoftheUrsaMinortoolisinformationgatheringandbrowsing. Leveragingoexistingtools:Weconsiderusingotheravailabletoolstoaugmentthefeaturesof sheetscapableofrichgraphicalpresentationofdata.byallowingtheinformationtobeunderstood byoneofthesespreadsheets,wecantakeadvantageofitsfeaturestocreatecharts,whilefocusing UrsaMinorthatweregardedas\notoriginalbutnicetohave".Forinstance,therearespread- tothetools(compilersandperformanceanalyzers)availableonthelocalplatform. 3DescriptionofUrsaMinor seeitthroughthetoolwithminimalmodications.wecanalsoenablethetooltoreadageneric Hence,wheneverweobtainnewtypesofinformationaboutthegivenprogramweshouldbeableto datale,sothatnewtypeofinformationcanbeunderstoodwithoutsignicantmodications. onthenewfunctionalityofursaminor. graphicalinterface,whichcanprovideselectiveviewsandcombinationsofthedata.figure1illustrates Thesesourcesincludetoolssuchascompilers,prolers,andsimulators.Itinteractswithusersthrougha 3.1Overview TheUrsaMinorprojectprovidestoolsthatassistparallelprogrammersineectivelywritingand tuningcodes.itprovidesuserswithinformationavailablefromvarioussourcesinacomprehensibleway. willdiscusshowourdesignobjectiveswererealizedintheconcretetool. Inthissection,weprovideanoverviewofUrsaMinor[PVAE97]anddescribeitsfunctionality.We Polariscompiler.TheUrsaMinortoolincludesasubroutineandloopneststructureanalyzer,also implementedusingthepolarisinfrastructure. tool[pet93,ke97].informationaboutwhichloopsareserialorparallelisprovidedbytheactual interactionbetweenursaminorandthevariousdatales. optionsareprovidedtoreadfromthevariousoriginalles,addtotheexistinginformationincrementally, explicitlybytheuserbeforeursaminorcanreadandcombinethem.oncetheyexist,severaltool notdiscussedfurtherinthispaper[eig93].maximumparallelismestimatesaresuppliedbythemax/p frominstrumentedprogramruns.thetoolperformingthisinstrumentationisapolaris-basedutility, Inthecurrentimplementation,theseinformationsourcesareavailableinlesthatneedtobecreated UrsaMinorcollectsandcombinesinformationfromvarioussources.Timinginformationisgathered 3

4 Calling Structure Analyzer Result Performance Results Information Sources Data Dependence Test Summary Simulation Report from Max/P Generated by Polaris-based Tools Other Information Sources Other Tools Saved DataBase Source File SpreadSheet open/save export storetheentiredatabase,orreadfromapreviouslysaveddatabase.infuturereleasesweplanto automatetheprocessofcreatingtheinformationsourcesby,forexample,invokingthecompilerondemand. Figure1:ComponentsoftheUrsaMinortoolandtheirinteractions. URSA MINOR UMD (Ursa DataBase) presentation/edit presentation/edit Loop Table View Call Graph View spectedwithaneditorandprinted.furthermore,theinformationcanbesavedinaformatthatcanbe isastorageunitthatholdsthecollectiveinformationaboutaprogramanditsexecutionresultsin acertainsystemenvironment.thisdatabaseisorganizedasatextle,whichcanoptionallybein- readbycommercialspreadsheets,providingarichersetofdatamanipulationfunctionsandgraphical Internally,UrsaMinorstoresinformationinUrsaMinor/MajorDatabases(UMD).AUMD interaction interaction representations. TheUrsaMinortooliswritteninJava.Thus,anyplatformonwhichtheJavaruntimeenvironment User prototypinguserinterfaces,whichenableustofocusonthedesignofthetoolfunctionality.furthermore, isavailablecanbeusedtorunthetool.itusesthebasicjavalanguagewithstandardapis,which thefunctionalityofursaminormoreclosely. newtypesofdatatothedatabase.thewindowingtoolkitsandutilitiesprovideagoodenvironmentfor Java,withitsnetworksupport,makesausefullanguageforrealizinganothergoalofthisproject:making beenrealizedintheursamajortool,whichisdiscussedinsection.inthenextsection,weexamine enhancestheportabilityofthetool.objectorientationinjavaallowsarelativelyeasyadditionof 3.2Functionality TheUrsaMinortoolpresentsinformationtotheuserthroughtwodisplaywindows:Aloopinformation tableandacallgraph.theuserinteractswiththetoolbychoosingmenuitemsormouse-clicking. availablethegatheredprogram,compilation,andperformanceresultstoremoteusers.thisgoalhas ofinvocationsofeachloop,theparentintheneststructure,andthemaximumdegreeofparallelism providedbymax/p[pet93,ke97].italsoindicateswhetheraloopisserialorparallelasdetectedby rently,thetabledisplaysinformationsuchastimingresultsfromvariousprogramruns,thenumber Polaris.Ifitisserial,thereasongivenbythecompilercanbedisplayedonmouse-clicking.InFigure2, Figure2showsthelooptableview,eachlinedisplayinginformationforanindividualloop.Cur-

5 theuserhasclickedonlooprestardo56toseethereasoninhibitingparallelization. programtuningprojects,anursaminorlooptableisusuallypresentallthetime.aftereachprogram view.also,ausercanrearrangecolumns,deletecolumns,sorttheentriesalphabeticallyorbasedon run,thenewlycollectedtiminginformationisincludedasanadditionalcolumninthelooptable.inthis theexecutiontime.byspecifyingareferencecolumn,speedupscanbecalculatedon-demand.inour Whenevernewinformationfromothertoolsbecomesavailable,theusercanaddcolumnsinthis Figure2:LoopTableViewoftheUrsaMinortool. overallprogram.eectsofprogrammodicationsonotherprogramsectionsbecomeobviousaswell. Themodicationmaychangetherelativeimportanceoftheloops,sothatsortingthembytheirnewest way,performancedierencescanbeinspectedimmediatelyforeachindividualloopaswellasforthe structure,theusercanzoominandout.thisdisplayhelpstounderstandtheprogramstructurefor taskssuchasinterchangingloopsorndingouterorinnercandidateparallelloops. executiontimeyieldsanewmost-time-consuminglooponwhichtheprogrammercanfocusnext. InFigurewehavereadthisformintothecommercialxess3spreadsheetprogram.Thisallowsone theuserisinspectingtheloopactfordo2inthisway.ifonewantsawiderviewoftheprogram subroutine,function,orloop.forexample,parallelloopsarerepresentedbygreenrectangles,andserial loopsbyredrectangles.clickingoneofthesewilldisplaythecorrespondingsourcecode.infigure3 subroutine,function,andloopnestinformationasshowninfigure3.eachrectanglerepresentseithera UrsaMinorcansavethedatabaseinaformatthatgenericspreadsheetprogramscanunderstand. AnotherviewofUrsaMinorprovidesthecallingstructureofagivenprogram,whichincludes toexploitthemanyoptionsandgraphicalrepresentationsofthistool.infiguretheuserhaschosen UrsaMajor[PE98]isanextensionoftheUrsaMinortool.BecausewechoseJavaasanimple- anexecutiontimegraphfortheprogrambdna,comparingtheperformanceofpolariswiththecompiler fromsunmicrosystems,(athirdlineindicating\linearspeedup"forreference). UrsaMajor:Web-basedevaluationofparallelapplications 5

6 Figure3:AnnotatedCallGraphViewoftheUrsaMinortool. Figure:Spread-SheetViewoftheUrsaMinortool. 6

7 mentationlanguage,itwasnaturaltocombineourtoolcapabilitieswiththerapidlyadvancinginternet canidentifypreciselytheeectofasourcecodechangeontheperformanceforboththemodiedcode codemodications,etc.thetoolhelpsrelateallthesepiecesofinformation,sothat,forexample,one thatcouldguidetheseusersinexploitingthenewmachines.ursamajorprovidesamethodologyof theirserialandparallelsourcecode,performanceimprovementsresultingfromcompilationorsource \learningbyexample"tobothlocalandremoteusers.newusersseeavarietyofsampleprograms, innon-expertusersandprogrammers.however,therearenoestablishedprogrammingmethodologies technologyand,inthisway,allowusersatremotesitestoaccessourexperimentaldata. sectionandtheoverallprogram. First,aordablemultiprocessorworkstationsandPCsarecurrentlyleadingtoasubstantialincrease Inextendingtheuseofourtooltoaworld-wideaudienceweareaddressingseveralnewissues: andthecomparisonofresultswiththoseobtainedbyothers.tothisend,manytestapplicationshave alargebodyofmeasurementsobtainedfromtheseprogramscanbefoundintheliteratureandon fromseveralpapers)andtheyhavetoundergosubstantialre-categorizationsandtransformations.in beenmadepubliclyavailableforstudyandbenchmarkingbybothresearchersandindustry.although Fromthebeginning,theabstractionofperformanceandprograminformationintoaformthatanswers addressingthisissue,theursamajorprojectiscreatingacomprehensivedatabaseofinformation. publicdatarepositories,itisusuallyextremelydiculttocombinethemintoaformmeaningfulfor newpurposes.inpartthisisbecausedataarenotreadilyavailable(i.e.,theyhavetobeextracted Second,acoreneedforadvancingthestateoftheartofcomputersystemsisperformanceevaluation thequestionsoftheobserverwasoneofourgoals.however,thisissuebecomesdrasticallymorecomplex asweconsiderlargedatarepositoriesorganizedintoamultitudeofdimensions.theinternettechnology anditscombinationwithhigh-performancecomputingtoolsopensthisnewrealmofquestionsand opportunities,whichwearebeginningtoexplorewithursamajor..1descriptionofursamajor UrsaMajorisaweb-basedtoolcapableofpresentingtheUrsaMinor/Majordatabasetoaremote networkingfeaturesandfororganizingthedataintoarepositorythatiseasytoaccessfromremote Majorrepository(UMR),whichwillbediscussedinthenextsection.UrsaMajorisavailableat UrsaMajor'smodulesfromthesecomponents.Inaddition,newmoduleswerecreatedforthetool's Websites.Thelatterincludesthedenitionofnamingschemeswithwhichinformationcanbefound basicbuildingblocksforursamajor.javaclassinheritancewasutilizedextensivelyfordeveloping user.figure5showsanoverallviewoftheinteractionsbetweenursamajor,auser,andtheursa intuitivelyandcaneasilyberelatedtootherinformation. UrsaMinor'sfacilitiesformanipulatingdatabasesandforcreatinggraphicaluserinterfacesare pagethroughjavaappletandisinvokedbyclickingabuttoninthewebpage. MajortoolisalmostidenticalwiththoseofUrsaMinor,butUrsaMajorisembeddedinaweb withursaminor,exceptthattheycannotsavelesonthelocaldisk.thelookandfeeloftheursa theumdsoftheirinterestbyexaminingthedescriptionsprovidedfortheavailableumds.umdsare thenretrievedbytheirurl.onceaumdisdisplayed,usersmayperformthesametasksastheydo istheaccesstotherepository.remotejavaapplicationscannotaccessdisklesdirectly.theyhaveto retrievedataintheformofwebdocuments.thisisduetojavasecurityrestrictions.usersmaychoose SinceitisbasedonUrsaMinor,UrsaMajoroersthesamebasicfunctionality.Onedierence amountofinformationisgathered.severalsucheortsareongoinginourgroup,hencetheumris Duringtheprocessofcompilingaparallelprogramandmeasuringitsperformance,aconsiderable.2UrsaMajorRepository(UMR) 7

8 Remote Server Ursa Major Applet UMR (Ursa Major Repository) Java Program Download DataBase Download PurdueUniversity,includingSPECandPerfectbenchmarks. continuouslybeingextended.itcurrentlycontainsseveralbenchmarksuitesthathavebeenstudiedat URSA MAJOR UMD (Ursa Database) reports,aswellasthetiminginformationofvariousprogramruns.findingparallelismstartsfrom Thespecicdataincludesstructuralprograminformation,resultsofprogramanalysis,simulation Figure5:InteractionprovidedbytheUrsaMajortool. presentation/edit database presentation/edit database Loop Table View Call Graph View interaction interaction leanddirectorynamesindicatingdatasuchastheprogramnames,platforms,compilers,optimization, andparallellanguages.tobeexible,theseextensionsarenothard-coded.instead,theyaredescribed ndinformationenteredbyotherusers.tothisend,therepositorystructureconsistsofextensionson lookingthroughthisinformationandlocatingpotentiallyparallelsectionsofcode.severaltoolsand methodologiesarebeingusedtogatherandorganizesuchdata[vggj+89,em93]. Oneissueindesigningtherepositorywastodenestorageschemesthatmakesiteasyforusersto User inacongurationlethatisreadbyursamajoratthestartofasession. WepresentearlyexperienceswithusingtheUrsaMajortoolandwithitsimplementation.Wehave.3ExperienceswithUrsaMajor usedthetoolinourresearchteam,onmultipleworkstationplatformsandalsopcsconnectedthrough modemsathome.ourteamincludesresearchersattwouniversities,sothatrealisticremoteaccesses wereinvolved.basedontheseexperienceswecanpicturescenariosofhowthedierentusercommunities canbesttakeadvantageofthetoolandwhatchallengesneedtobeaddressedtomakeitevenmore grammers,andresearchersinterestedinperformanceevaluationandbenchmarking.obviouslythese usefulinthefuture. categoriescanoverlap.forbeginners,thetoolsupportsamethodologyof\learningbyexample".new programmersstartbygettingthegeneralfeelfortherepository.thisisbestdonestartingwiththe callgraphviewandclickingonseveralnodesinthisgraphtoinspectthesourceprograms.togetmore UrsaMajortargetsseveralaudiences.Theyincludenoviceparallelprogrammers,advancedpro- 8

9 compareserialandparallelprogramversions.ursamajorsupportsthisbyprovidingthelooptable view.sourcecodecorrespondingtoserialandtheparallelvariantcanbeopened.thelooptablealso insightsaboutanindividualprogramtheusernowcanstepthroughthemosttime-consumingloopsand givesthenewuserarstideaofhowprogramsneedtobetransformedtoruninparallelandwhat showstimingsofthetwovariantsgivingtheuserarstviewofthespeedupsobtainedbyeachloop.the improvementsbycombiningtheperspectivesfrombothperformanceevaluationandcompileranalysis performanceimprovementcanbeobtained. spectionofthereasonswhycertainparallelloopsorprogramsectionsperformwellorpoorlyinmore toolcancomputeanddisplaythesespeedupnumbersasanoption.comparingtheseprogramvariants ofinformationkeptintheursamajorrepositoryandfacilitatingaccesstothisinformationinvarious detailandwhyacodesectionisnotparallel.inthisway,usersmayidentifythebottleneckandpossible results. dimensions.evenwithinourresearchgrouptheavailabilityoftherepositoryenabledmanydierent Theadvancedprogrammermaybenetfromthistoolbyexploitingthefeaturesallowingthein- ongoingeort. entsubroutinesandloopswithinaprogram,andscalabilitystudiesovernumbersofprocessorsanddata setsizes.increasingthesupportforinspectingourdatabasefromthesevariousanglesisanimportant studies,suchasarchitecturalcomparisons,comparisonsofdierentcompilers,dierentprograms,dier- UrsaMajorfurtherservestheresearchcommunityingeneralbymakingavailablethelargeamount performedontheperfectbenchmarkscodearc2d,ispresentedhereasourrstcasestudy. UrsaMinorisusedinthesearchforexplanationsofthesedierences.Anexampleofsuchasearch codeswithvariousdirectivesets.iftheperformanceresultsofthesecodesaresignicantlydierent, pileroutputrepresentation[vos97].indoingso,wehaveexpressedtheparallelisminseveralbenchmark 5CaseStudies 5.1ExperimentswiththeARC2DApplication Inacurrentstudy,wearecomparingparalleldirectivelanguagesfortheirsuitabilityasaportablecom- astheexecutiontimemeasuredbytheinstrumentation,itiseasilydeterminedwhensuchperturbation occurs.inarc2d,11ofthe19loopshadaninstrumentationoverheadofmorethan.1%oftheloop noticeablyimpactthemeasuredperformance.usingthenumberoftimeseachloopisexecuted,aswell executiontime.wechose.1%asthecutotoensurethattheinstrumentedtimingmeasurementsstill gatheredbyursaminorandtransformedintoaformwhichisreadablebycommercialspreadsheet packagessuchasexcelandxess3.oneconcernwithinstrumentationisthattheassociatedoverheadwill onaprocessorultrasparcworkstationwasdone.theresultsofthisinstrumentationwasthen reectedtheprogramperformancewithhighaccuracy.removingtheinstrumentationfromthese11 First,asabase-linemeasurement,aloopbyloopproleoftheserialversionofthecodeexecuted averageexecutiontimesforcomputingtheoverhead.infuturereleasesofthetoolthiscomputationwill parallelizedversionsoftheseloopswereusedtocomparetheperformanceofseveralparalleldirective loops,reducedthetotalexecutiontimeoftheprogramby6%.ursaminorcurrentlyprovidesthe befullyautomated. languages.themajorloopsinarc2dparallelizedbypolarisarefilerxdo19,stepfxdo21and OpenMPindustrystandard[OMP97].BrowsingthroughtheperformanceresultsdisplayedbyUrsa theseloopsintheserialversioncanbeseeninfigure6. STEPFXdo23.TheidenticationoftheseloopswasstraightforwardgiventhatUrsaMinorpresented dialectandtheotherusingtheportablekap/prodirectiveset[kuc88],acloserelativeofthenew theexecutiontimesofeachloopaswellasannotateditasparallelorserial.therelativeimportanceof Additionally,themosttime-consumingloopswereidentiedintheserialcode.ThePolaris- Minoritwasseenthatonprocessors,theKAP/Prodirectivelanguageexhibitedsuperiorperformance. TheparallelismfoundbyPolariswasexpressedintwoforms.OneusingthenativeSunSPARC 9

10 Figure6:PercentageofexecutiontimespentinmajorloopsofARC2D. STEPFX do23 (11.7%) STEPFX do21 (1.9%) FILERX do19 (6.%) reason.loopinterchangingwasbeingappliedtomanyoftheloopnestsinthekap/prodirectiveversion Furthermore,byaddingtheloop-by-loopproleofARC2D,asparallelizedbytheSunnativecompiler, loopsinthekap/proversionwhencomparingthe1processorparallelexecutiontotheexecutionof aninterestingphenomenonwasdiscovered:asignicant\negativeoverhead"existedformanyofthe theuntransformedcode.apparently,sequentialoptimizationswereperformedinthekap/proversion SunSPARCdirectives.TheperformanceofthethreemajorloopsisshowninFigure7. intheloopsfoundtobeparallelbythenativecompiler,butnotinthepolarisversionwhichusedthe whichwerenotperformedintheserialversion.interestingly,thissameoptimizationwasoftenperformed Usingthesourcecodebrowsingcapabilities,aside-by-sidecomparisonoftheloopnestsuncoveredthe Others interchangingwasnotdisabledwhenparallelizingthecodewiththenativesunparallelizingcompiler; bytheback-endcompiler.theuseofthesunsparcdirectivesinhibitedthistransformation.loop (71.%) wereimperfectlynestedintheoriginalsource,butweretransformedintoaperfectnestbypolaris. TheapplicationofforwardsubstitutionanddeadcodeeliminationbyPolariscreatedperfectlynested parallelizingcompilerwasabletoidentifythesameamountofparallelismaspolaris,itdidnotapply loops,whichtheback-endcompilerwasthenabletointerchange.therefore,althoughthenativesun furtheroptimizations.figure8showstheperformanceofthethreeparallelversionsofarc2dexecuted howeveritwasappliedlessfrequently.foramoredetaileddiscussionofthisphenomenonandothers onprocessorsoftheultrasparc.thisgurealsoshowstheperformancethatwouldbeobtainedin uncoveredduringtheanalysisofarc2d,pleasereferto[vos97]. thesunsparcdirectiveversioniftheinterchanginghadbeendone. structurerepresentation,showedthatthetwomostsignicantloopsstepfxdo21andstepfxdo23 Afurtheranalysisoftheserialsource,thePolaristranslatedversions,andtheirgraphicalloop quicklyidentied.theoftentedioustaskoftabularizingprolingresultswasperformedautomatically waseasilyperformedwiththebrowsingfacilities.thegraphspresentedinfigures6through8can graphingfunctions. andtheidenticationoftheparallelloopsinthistablewasmadeobvious.thenestingstructureof begeneratedbyexportingtheursaminor/majordatabasetothexess3spreadsheetandusingits severalversionsofthesourcecodeforeachloopnestwasoftennecessary,andaside-by-sidecomparison theloopstructurewasasignicantaidinquicklyidentifyingthisphenomenon.adetailedstudyofthe theloopswasamajorfactorintheperformanceofthiscode,andursaminor'sgraphicaldisplayof UrsaMinorallowedthecharacteristicsresponsiblefortheperformancedierencesinARC2Dtobe obtainedonaprocessorsparcstation2,a6processorultrasparcenterprise,a16processorsilicongraphicspowerchallengeanda32processorsorigin2havebeenmadeavailableasumdsatures,canbeinteractivelyexploredthroughtheursamajorwebpage.performancemeasurements Thefullresultsofthisstudy,performedon8benchmarkprogramsacrossmultiprocessorarchitec- 1

11 (a) (b) (c) (d) (e) (f) 8 Figure7:LoopperformanceofARC2DonanUltraSPARC:(a)ExecutiontimeofFILERXdo19,(b) SpeedupofFILERXdo19,(c)ExecutiontimeofSTEPFXdo21,(d)SpeedupofSTEPFXdo21,(e) ExecutiontimeofSTEPFXdo23and(f)SpeedupofSTEPFXdo Execution Time (sec) Execution Time (sec) Execution Time (sec)12 ser Number of Processors ser Number of Processors ser Number of Processors Speedup Speedup Speedup Number of Processors Number of Processors 1 Native Parallelizer Polaris+Native Directives Polaris+KAP/Pro Directives Number of Processors Figure8:PerformanceofARC2DonProcessorsofUltraSPARC Native Sun Parallelizer Polaris+Sun Directives 2 +Perfect Nest Interchange +Imperfect Nest Interchange Polaris+KAP/Pro Directives 1

12 howthecomputationalcomplexityoftheoverallapplicationsuitescaleswiththenumberofprocessors 5.2ExperimentwiththeSeismicApplicationSuite Asthesecondcasestudy,weintroduceanotherprojectthatcharacterizesandanalyzeslarge-scope thatsite.foradetaileddescriptionoftheseresultsreferto[vos97]. [MH93],aseismicactivitysimulationprogramconsistingof2,linesofFortrancode.TheSeismic BenchmarkSuitecontainsadeephierarchyofnestedsubroutinesandloops.Ourgoalistounderstand industrialapplications[ae97].oneoftheprogramsweconsideredwastheseismicbenchmarksuite providesaverageloopexecutiontimesaswellasaloop'sparentinthecallingstructure.withcodes aslargeastheseismicsuitethesimpletaskoflocatingthebeginningandendingofloopsbecomes andwiththeinputdataspace.here,wewillbrieydescribehowtheursaminortoolcanbeofhelp loopfromactualmeasurements.inordertodothisweusethelooptableviewinursaminorwhich executiontime,exclusiveofanyinner-loops,isestimatedbyobtaininganexpressionforthenumberof iterationstheloopwillexecuteandcombiningthisexpressionwiththeaveragetimeperiterationofthe intheprocessofcharacterizingalargeapplication. cumbersomeandpronetohumanerrors.ursaminorgreatlysimpliesthistaskandprovidesavisual descriptionoftheloopnesthierarchywithitscallgraphview. Tocharacterizeanapplication'sexecutiontimewesumthetimescontributedbyeachloop.Aloop's Figure9:Actualmeasurementsofloopexecutiontimeswerecomparedwithpredictedtimestodetermine 7 theaccuracyofthemodelonaloop-by-loopbasis.theseparatecolorsrepresenttheloopsofthisseismic 6 phase.theactualmeasurementsweregatheredusinga32-processornodeofansgi/crayorigin2 ofncsaattheuniversityofillinois. 5 3 characterizationandlocatethepointsneedingrenement.figure9comparesactualmeasurementswith ourpredictedtimesforoneseismicprocessingphase(calleddepthmigration)asthenumberofprocessors 2 increasesfrom1to32.ursaminoraidedingatheringthedatafromboththemeasurementsandour 1 modelsothateachloop'sperformancecouldbeanalyzedindividually.loopsthatscaleddierently fromthemeasuredtimingswereeasytond.ourmodelcouldthenbemodiedformoreaccurate Afterwecharacterizedthecode'sperformance,weusedUrsaMinortodeterminetheaccuracyofour F M F M F M F M F M F M F = Forecasted, 12 M = Measured Number Processors Time (seconds) Phase : Depth Migration

13 whenthenumberofprocessorsincreased. predictions.weusedthisprocesstotestourmodel'sscalabilitywhenthedatasizeincreasedaswellas importingintoanxess3spreadsheet,inwhichweproducedgraphsvisuallydepictingthescalability machineslarger(moreprocessors)thanwecurrentlyhaveavailableandtoinputdatasizesappropriate dominatedbythecomputationtime(becauseofthisthecurves\total"and\comp"overlap). Figure1:ForecastedperformanceoftheSeismicSuiteasthemachinesizeisscaledup.Thecurves oftheapplication.figure1showsextrapolationresultsforoneseismicprocessingphase,againdepth forsuchlargemachines.databasesofpredictedexecutiontimeswereexportedfromursaminorfor dividethetotalexecutiontimeintocomputation,communication,anddiskiotimes.thetotaltimeis migration,asthenumberofprocessorsisincreasedfrom1to2,8processors.thedatasetisonewhich woulduse3terabytesofdiskspace. ThenalgoalofourcharacterizingprocesswasextrapolatingtheSeismicSuite'sperformanceto wellaloop-parallelversionoftheprogramwouldperformusingpolarisasastartingpoint.ursaminor program.asoriginallywritten,theseismicbenchmarkisamessage-passingcode.weinvestigatedhow parallelexecutionoverhead. calculatedthespeedupofourloop-parallelprogramforeachloop,aggingtheloopswithspeedupsbelow 1.TheseloopsweretheninvestigatedfurthertoimprovetheirautomaticparallelizationbyPolaris.If useofursamajor.measurementsweregatheredusingthesgi/crayorigin2atncsa. noimprovementscouldbemade,weforcedalooptoexecuteseriallysothatitwouldnotincurany AnotherobjectiveoftheSeismicBenchmarkcasestudywastoproduceawell-performingloop-parallel 6Conclusion Wehavepresentedanon-goingprojectthatprovidestoolsandmethodologiesforparallelprogram developmentandperformanceevaluation.ursaminorandursamajorsupportusermodelsof \parallelprogrammingbyexamples"forbeginnersandinteractivecompilationandperformancetuning ThedatafromtheSeismicBenchmarkcasestudyiscurrentlyavailabletooutsideusersthroughthe forexperts.theyalsoserveasaprogramandbenchmarkdatabaseforcomputingsystemsresearch.the 13 Time (seconds, log scale) 1x1 9 1x1 8 1x1 7 1x1 6 1x1 5 1x1 1x1 3 1x1 2 1x1 1 Phase : Depth Migration 1x Number of Processors Total Comp Comm Disk IO Disk Reads Disk Writes

14 toolsintegrateinformationavailablefromperformanceanalysistools,compilers,simulators,andsource Keepingclosetogetherthetooldesignprojectsandapplicationcharacterizationeortswillensurethe programstoadegreenotprovidedbyprevioustools.ursamajorcanbeexecutedontheworld-wide practicalityofourtoolinthefuture. Web,fromwhereagrowingrepositoryofinformationcanbeviewed. toolsandtheiruserviews.forexample,wewillincludeimprovedcompilerexplanationswhycertain compilerortoperformcertaintransformationsbyhand.anotherimportantgoalisthesupportfor optimizationswereorwerenotperformed.thisenablestheprogrammertoinputmissingdatatothe ToolcapabilitiesneededintheseeortsarebeingintegratedinbothUrsaMinorandUrsaMajor. asthecharacterizationandanalysisofrealapplicationsandthedevelopmentofparallelizingcompilers. Severalenhancementsareplannednext.Newcategoriesofinformationwillbeintegratedintothe TheUrsatoolfamilyisevolvinginaneed-drivenway.Itsdevelopersarealsoinvolvedinprojectssuch thetool'sservicetoaworld-wideaudience. References usermethodologies.asalong-termgoalweenvisionfacilitiesthatallowonetoquerytheinformation [AE97]BrianArmstrongandRudolfEigenmann.Performanceforecasting:Characterizationofap- repositorydirectlyforsuggestedimprovementsofprograms,compilers,orarchitectures.bettersupport oeredbythenewinternettechnology,continuousfeedbackfromitsusercommunitywillhelpimprove forthetool'swebresponseisanotherongoingeort.aswehaveonlybeguntoexplorethepotential [AO88]J.AmbrasandV.O'Day.MicroScope:AKnowledge-BasedProgrammingEnvironment. [ASM89]BillAppelbe,KevinSmith,andCharlesMcDowell.Start/Pat:AParallel-Programming putinglaboratory,february97. Toolkit.IEEESoftware,6():29{38,July1989. IEEESoftware,pages5{58,May1988. dueuniversity,schoolofelectricalandcomputer,engineering,high-performancecom- plicationsoncurrentandfuturearchitectures.technicalreportece-hpclab-9722,pur- [BKK+89]V.Balasundaram,K.Kennedy,U.Kremer,K.McKinley,andJ.Subhlok.TheParaScope [BDE+96]W.Blume,R.Doallo,R.Eigenmann,J.Grout,J.Hoeinger,T.Lawrence,J.Lee,D.Padua, [BST86]G.Bruno,P.Spiller,andI.Tota.AISPE:AnAdvanced,IndustrialSoftware-Production Y.Paek,B.Pottenger,L.Rauchwerger,andP.Tu.ParallelprogrammingwithPolaris.IEEE editor:aninteractiveparallelprogrammingtool.ininternationalconferenceonsupercomputing,pages5{55,1989. Computer,pages78{82,December1996. Environment.ProceedingsofComputerSoftwareandApplicationsConf.,pages9{99, [EM93]RudolfEigenmannandPatrickMcClaughry.PracticalToolsforOptimizingParallelPrograms.Presentedatthe1993SCSMulticonference,Arlington,VA,March27-April1, Computers.ConferenceProceedings,ICS'93,Tokyo,Japan,pages27{36,July2-22, [Eig93]RudolfEigenmann.TowardaMethodologyofOptimizingProgramsforHigh-Performance [HAA+96]M.W.Hall,J.M.Anderson,S.P.Amarasinghe,B.R.Murphy,S.-W.Liao,E.Bugnion, andm.s.lam.maximizingmultiprocessorperformancewiththesuifcompiler.ieee Computer,pages8{89,December

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