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ASTUDYOFBRANCHPREDICTIONSTRATEGIESJAMESESMITHCONTROLDATACORPORATIONARDENHILLSMINNESOTAABSTRACTINHIGHPERFORMANCECOMPUTERSYSTEMSPERFORMANCELOSSESDUETOCONDITIONALBRANCHINSTRUCTRONSCANBEMINRMIZEDBYPREDICTINGABRANCHOUTCOMEANDFETCHING,DECODING,AND/ORISSUINGSUBSEQUENTINSTRUCTIONSBEFORETHEACTUALOUTCOMEISKNOWNTHISPAPERDISCUSSESBRANCHPREDICTIONSTRATEGIESWRTHTHEGOALOFMAXTMIZINGPREDICTIONACCURACYFIRSTCURRENTLYUSEDTECHNIQUESAREDISCUSSEDANDANALYZEDUSINGINSTRUCTIONTMCEDATATHEN,NEWTECHNIQUESAREPROPOSEDANDARESHOWNTOPROVIDEGREATERACCURACYANDMOREFLEXIBILITYATLOWCOSTINTRODUCTIONITISWELLKNOWN13,10THATINAHIGHLYPARALLELCOMPUTERSYSTEM,BRANCHINSTRUCTIONSCANBREAKTHESMOOTHFLOWOFINSTRUCTIONFETCHINGANDEXECUTIONTHISRESULTSINDELAY,BECAUSEABRANCHTHATISTAKENCHANGESTHELOCATIONOFINSTRUCTIONFETCHESANDBECAUSETHEISSUINGOFINSTRUCTIONSMUSTOFTENWAITUNTILCONDITIONALBRANCHDECISIONSAREMADETOREDUCEDELAY,ONECANATTEMPTTOPREDICTTHEDIRECTIONTHATABRANCHINSTRUCTIONWILLTAKEANDBEGINFETCHING,DECODING,OREVENISSUINGINSTRUCTIONSBEFORETHEBRANCHDECISIONISMADEUNFORTUNATELY,AWRONGPREDICTIONMAYLEADTOMOREDELAYIF,FOREXAMPLE,INSTRUCTIONSONTHECORRECTBRANCHPATHNEEDTOBEFETCHEDORPARTIALLYEXECUTEDINSTRUCTIONS0,”THEWRONGPATHNEEDTOBEPURGEDTHEDISPARITYBETWEENTHEDELAYFORACORRECTLYPREDICTEDBRANCHANDANINCORRECTLYPREDICTEDBRANCHPOINTSTOTHENEEDFORACCURATEBRANCHPREDICTIONSTRATEGIESTHISPAPERDISCUSSESBRANCHPREDICTIONSTRATEGIESWITHTHEGOALOFMAXIMIZINGTHELIKELIHOODOFCORRECTLYPREDICTINGTHEOUTCOMEOFABRANCHFIRSTPREVIOUSLYSUGGESTEDBRANCHPREDICTIONTECHNIQUESAREDISCUSSEDOWINGTOTHELARGENUMBEROFVARIATIONSANDCONFIGURATIONS,ONLYAFEWREPRESENTATIVESTRATEGIESHAVEBEENSINGLEDOUTFORDETAILEDSTUDY,ALTHOUGHSEVERALAREMENTIONEDTHEN,NEWTECHNIQUESAREPROPOSEDTHATPROVIDEMOREACCURACY,LESSCOST,ANDMOREFLEXIBILITYTHANMETHODSUSEDCURRENTLYBECAUSEOFTHEWIDEVARIATIONINBRANCHINGBEHAVIORBETWEENDIFFERENTAPPLICATIONS,DIFFERENTPROGRAMMINGLANGUAGES,ANDEVENINDIVIDUALPROGRAMS,THEREISNOGOODANALYTICMODELFORSTUDYINGBRANCHPREDICTIONFORTHISREASON,WEUSEDINSTRUCTIONTRACEDATATOMEASUREEXPERIMENTALLYTHEACCURACYOFBRANCHPREDICTIONSTRATEGIESORIGINALLY,TENFORTRANPROGRAMS,PRIMARILYSCIENTIFIC,WERECHOSENITWASFOUND,HOWEVER,THATSEVERALWEREHEAVILYDOMINATEDBYINNERLOOPS,WHICHMADETHEMVERYPREDICTABLEBYEVERYSTRATEGYCONSIDEREDTWOPROGRAMS,SC12ANDADVAN,WERECHOSENFROMTHISINNERLOOPDOMINATEDCLASSFOUROTHERPROGRAMSWERENOTASHEAVILYDOMINATEDBYINNERLOOPSANDWERELESSPREDICTABLEALLWERECHOSENFORTHESTUDYTHESIXFORTRANPROGRAMSUSEDINTHISSTUDYWERE1ADVANCALCULATESTHESOLUTIONOFTHREESIMULTANEOUSPARTIALDIFFERENTIALEQUATIONS2SCL23SINCOSPERFORMSMATRIXINVERSIONCONVERTSASERIESOFPOINTSFROMPOLARTOCARTESIANCOORDINATES4SORTSTSORTSALISTOF10,DDOINTEGERSUSINGTHESHELLSORTALGORITHM95GIBSONANARTIFICIALPROGRAMTHATCOMPILESTOINSTRUCTIONSTHATROUGHLYSATISFYTHESOCALLEDGIBSONMIX56TBLLNKPROCESSESALINKEDLISTANDCONTAINSAVARIETYOFCONDITIONALBRANCHESTHEPROGRAMSWERECOMPILEDFORACDCCYBEB170ARCHITECTURENOTETHATOTHERTHANADVANANDSC12THETESTPROGRAMSWERECHOSENFORTHEIRUNPREDICTABILITYANDTHATAMORETYPICALSCIENTIFICMIXWOULDCONTAINMOREPROGRAMSLIKEADVANANDSCL20149711LLBL/OOOO/O13575Q1981IEEE202BECAUSEOFTHEMETHODUSEDFOREVALUATINGPREDICTIONSTRATEGRES,ANYCONCLUSIONSREGARDINGTHEIRRELATIVEPERFORMANCEMUSTBECONSIDEREDINLIGHTOFTHEAPPLICATIONAREAANDTHELANGUAGEUSEDHERENEVERTHELESS,THEBASICCONCEPTSANDTHESTRATEGIESAREOFBROADERINTEREST,BECAUSEITISRELATIVELYSTRAIGHTFORWARDTOGENERATEINSTRUCTIONTRACESANDTOMEASUREPREDICTIONACCURACYFOROTHERAPPLICATIONSORLANGUAGESRESULTSPUBLISHEDPREVIOUSLYINTHISAREAAPPEARINSHUSTEK6,WHOUSEDINSTRUCTIONTRACEDATATOEVALUATESTRATEGIESFORTHEIBMSYSTEM360/370ARCHITECTURE1BBETT’DESCRIBEDTHEINSTRUCTIONPIPELINEOFTHEMU5COMPUTERANDGAVEEXPERIMENTALRESULTSFORAPARTICULARBRANCHPREDICTIONSTRATEGYARATHERSOPHISTICATEDBRANCHPREDICTORHASBEENDESCRIBEDFORTHESLPROCESSOR,SBUTASOFTHISWRITING,NOINFORMATIONAPPEARSTOHAVEBEENPUBLISHEDREGARDINGITSACCURACYBRANCHPREDICTIONSTRATEGIESHAVEALSOBEENUSEDINOTHERHIGHPERFORMANCEPROCESSORS,BUT,AGAIN,EXPERIMENTALRESULTSHAVENOTBEENPUBLISHEDOURSTUDYBEGINSINTHENEXTSECTION,WITHTWOBRANCHPREDICTIONSTRATEGIESTHATAREOFTENSUGGESTEDTHESESTRATEGIESINDICATETHESUCCESSTHATCANREASONABLYBEEXPECTEDTHEYALSOINTRODUCECONCEPTSANDTERMINOLOGYUSEDINTHISPAPERSTRATEGIESAREDIVIDEDINTOTWOBASICCATEGORIES,DEPENDINGONWHETHERORNOTPASTHISTORYWASUSEDFORMAKINGAPREDICTIONINSUBSEQUENTSECTIONS,STRATEGIESBELONGINGTOEACHOFTHECATEGORIESAREDISCUSSED,ANDFURTHERREFINEMENTSINTENDEDTOREDUCECOSTANDINCREASEACCURACYAREPRESENTEDIEVELSOFCONFIDENCEAREATTACHEDTOBRANCHPREDICTIONSTOMINIMIZEDELAYWHENTHEREAREVARYINGDEGREESTOWHICHBRANCHOUTCOMESCANBEANTICIPATEDFOREXAMPLE,PREFETCHINGINSTRUCTIONSISONEDEGREE,PREISSUINGTHEMISANOTHERCONCLUSIONSAREGIVENINTHEFINALSECTIONTWOPRELIMINARYPREDICTIONSTRATEGIESBRANCHINSTRUCTIONSTESTACONDITIONSPECIFIEDBYTHEINSTRUCTIONIFTHECONDITIONISTRUE,THEBRANCHISTAKENINSTRUCTIONEXECUTIONBEGINSATTHETARGETADDRESSSPECIFIEDBYTHEINSTRUCTIONIFTHECONDITIONISFALSE,THEBRANCHISNOTTAKEN,ANDINSTRUCTIONEXECUTIONCONTINUESWITHTHEINSTRUCTIONSEQUENTIALLYFOLLOWINGTHEBRANCHINSTRUCTIONANUNCONDITIONALBRANCHHASACONDITIONTHATISALWAYSTRUETHEUSUALCASEORISALWAYSFALSEEFFECTIVELY,APASSBECAUSEUNCONDITIONALBRANCHESTYPICALLYARESPECIALCASESOFCONDITIONALBRANCHESANDUSETHESAMEOPERATIONCODES,WEDIDNOTDISTINGUISHTHEMWHENGATHERINGSTATISTICS,ANDHENCE,UNCONDITIONALBRANCHESWEREINCLUDEDASTRAIGHTFORWARDMETHODFORBRANCHPREDICTIONISTOPREDRCTTHATBRANCHESAREEITHERALWAYSTAKENORALWAYSNOTTAKENBECAUSEMOSTUNCONDITIONALBRANCHESAREALWAYSTAKEN,ANDLOOPSARETERMRNATEDWITHBRANCHESTHAT,ARETAKENTOTHETOPOFTHELOOP,PREDICTINGTHATALLBRANCHESARETAKENRESULTSTYPICALLYINASUCCESSRATEOFOVER50STRATEGY1LPREDICTTHATALLBRANCHESWILLBETAKENFIGURE1SUMMARIZESTHERESULTSOFUSINGSTRATEGY1ONTHESIXFORTRANBENCHMARKSFROMFIGURE1,ITISEVIDENTTHATTHEMAJORITYOFBRANCHESARETAKEN,ALTHOUGHTHESUCCESSRATESVARYWIDELYFROMPROGRAMTOPROGRAMTHISPOINTSTOONEFACTORTHATMUSTBECONSIDEREDWHENEVALUATINGPREDICTIONSTRATEGIESPROGRAMSENSITIVITYTHEALGORITHMBEINGPROGRAMMED,ASWELLASTHEPROGRAMMERANDTHECOMPILER,CANINFLUENCETHESTRUCTUREOFTHEPROGRAMAND,CONSEQUENTLY,THEPERCENTAGEOFBRANCHESTHATARETAKENHIGHPROGRAMSENSITIVITYCANLEADTOWIDELYDIFFERENTPREDICTIONACCURACIESTHIS,INTURN,CANRESULTINSIGNIFICANTDIFFERENCESINPROGRAMPERFORMANCETHATMAYBEDIFFICULTFORTHEPROGRAMMEROFAHIGHLEVELLANGUAGETOANTICIPATESTRATEGY1ALWAYSMAKESTHESAMEPREDICTIONEVERYTIMEABRANCHINSTRUCTIONISENCOUNTEREDBECAUSEOFTHIS,STRATEGY1ISCALLEDSFAFICITHASBEENOBSERVEDANDDOCUMENTED,6HOWEVER,THATTHELIKELIHOODOFACONDITIONALBRANCHINSTRUCTIONATAPARTICULARLOCATIONBEINGTAKENISHIGHLYDEPENDENTONTHEWAYTHESAMEBRANCHWASDECIDEDPREVIOUSLYTHISLEADSTODYNAMICPREDICTIONSTRATEGIESINWHICHTHEPREDICTIONVARIES,BASEDONBRANCHHISTORYSTRATEGY2LPREDICTTHATABRANCHWILLBEDECIDEDTHESAMEWAYASITWASONITSLASTEXECUTIONIFITHASNOTBEENPREVIOUSLYEXECUTED,PREDICTTHATITWILLBETAKENTHERESULTSFIGURE2OFUSINGSTRATEGY2,INDICATETHATSTRATEGY2GENERALLYPROVIDESBETTERACCURACYTHANSTRATEGY1UNFORTUNATELY,STRATEGY2ISNOTPHYSICALLYREALIZABLE,BECAUSETHEORETICALLY,THEREISNOBOUNDON203THENUMBERONRNDRVIDUALBRANCHRNSTRUCTRONSTHATAPROGRAMMAYCONTAININPRACTICEHOWEVER,ITMAYBEPOSSRBLETORECORDTHEHISTORYOFALIMITEDNUMBEROFPASTBRANCHESSUCHSTRATEGRESAREDISCUSSEDINASUBSEQUENTSECTRONSTRATEGIES1AND2PROVIDESTANDARDSFORJUDGINGOTHERBRANCHPREDICTIONSTRATEGRESSTRATEGY1ISSIMPLEANDINEXPENSIVETOIMPLEMENT,ANDANYSTRATEGYTHATISSERIOUSLYBEINGCONSIDEREDFORUSESHOULDPERFORMATLEASTATTHESAMELEVELASSTRATEGY1STRATEGY2ISWIDELYRECOGNIZEDASBEINGACCURATE,ANDIFAFEASIBLESTRATEGYCOMESCLOSETOOREXCEEDSTHEACCURACYOFSTRATEGY2THESTRATEGYISABOUTASGOODASCANREASONABLYBEEXPECTEDSTRATEGY1ISAPPARENTLYMOREPROGRAMSENSITIVETHANSTRATEGY2EVIDENCEOFTHISISTHEWIDEVARIATIONINACCURACYFORSTRATEGY1ANDTHEMUCHNARROWERVARIATIONFORSTRATEGY2FIGURES1AND2STRATEGY2HASAKINDOFSECONDORDERPROGRAMSENSITIVITY,HOWEVER,INTHATABRANCHTHATHASNOTPREVIOUSLYBEENEXECUTEDISPREDICTEDTOBETAKENLOWERPROGRAMSENSITIVITYFORDYNAMICPREDICTIONSTRATEGIESISTYPICAL,ASRESULTSTHROUGHOUTTHISPAPERSHOWFINALLY,ITISINTERESTINGTHATONEASPECTOFBRANCHBEHAVIORLEADSOCCASIONALLYTOBETTERACCURACYWITHSTRATEGY1THANSTRATEGY2OFTEN,APARTICULARBRANCHINSTRUCTIONISPREDOMINATELYDECIDEDONEWAYFOREXAMPLE,ACONDITIONALBRANCHTHATTERMINATESALOOPISMOSTOFTENTAKENSOMETIMES,HOWEVER,ITISDECIDEDTHEOTHERWAYWHEN“FALLINGOUTOFTHELOOP”THESEANOMALOUSDECISIONS,ARETREATEDDIFFERENTLYBYSTRATEGIES1AND2STRATEGY1,IFITISBEINGUSEDONABRANCHTHATISMOSTOFTENTAKEN,LEADSTOONEINCORRECTPREDICTIONFOREACHANOMALOUSNOTTAKENDECISIONSTRATEGY2LEADSTOTWOINCORRECTPREDICTIONSONEFORTHEANOMALOUSDECISIONANDONEFORTHESUBSEQUENTBRANCHDECISIONTHEHANDLINGOFANOMALOUSDECISIONSEXPLAINSTHOSEINSTANCESINWHICHSTRETEGY1OUTPERFORMSSTRATEGY2ANDINDICATESTHATTHEREMAYEXISTSOMESTRATEGIESTHATCONSISTENTLYEXCEEDTHESUCCESSRATEOFSTRATEGY2STATICPREDICTIONSTRATEGIESSTMTEGY1ALWAYSPREDICTTHATABRANCHISTAKENANDITSCONVERSEALWAYSPREDICTTHATABRANCHISNOTTAKENARETWOEXAMPLESOFSTATICPREDICTIONSTRATEGIESAFURTHERREFINEMENTOFSTRATEGY1‘ISTOMAKEAPREDICTIONBASEDONTHETYPEOFBRANCH,DETERMINED,FOREXAMPLE,BYEXAMININGTHEOPERATIONCODETHISISTHESTRATEGYUSEDINSOMEOFTHEIBMSYSTEM360/370MODELS9ANDATTEMPTSTOEXPLORTPROGRAMSENSRUVMESBYOBSERVRNG,FOREXAMPLE,THATCERTAINBRANCHTYPESAREUSEDTOTERMINATELOOPS,WHILEOTHERSAREUSEDINIFTHENELSETYPECONSTRUCTSSTRATEGYLAPREDICTTHATALLBRANCHESWITHCERTAINOPERATIONCODESWILLBETAKEN;PREDICTTHATTHEOTHERSWILLNOTBETAKENTHESIXCYBER170FORTRANPROGRAMSWEREEXAMINED,ANDITWASFOUNDTHAT“BRANCHIFNEGATIVE”,“BMNCHIFEQUAL”,AND“BRANCHIFGREATERTHANOREQUAL”AREUSUALLYTAKEN,SOTHEYAREALWAYSPREDICTEDTOBETAKENOTHEROPERATIONCODESAREALWAYSPREDICTEDTOBENOTTAKENTHISSTRATEGYISSOMEWHATTUNEDTOTHESIXBENCHMARKS,BECAUSEONLYTHEBENCHMARKSWEREANALYZEDTODETERMINEWHICHOPCODESSHOULDBEPREDICTEDTOBETAKENFORTHISREASON,THERESULTSFORSTRATEGYLAMAYBESLIGHTLYOPTIMISTICFIGURE3SHOWSTHERESULTSFORSTRATEGYLAWHENITWASAPPLIEDTOTHECY170PROGRAMSGENERALLY,GREATERACCURACYWASACHIEVEDWITHSTRATEGYLATHANWITHSTRATEGY1THELARGESTINCREASEWASINTHEGIBSONPROGRAMINWHICHTHEPREDICTIONACCURACYWASIMPROVEDFROM654TO985THEONLYPROGRAMSHOWINGADECREASEINACCURACYWASTHESINCOSPROGRAMINWHICHTHEREWASADROPFROM802TO657THECHANGESINBOTHTHEGIBSONANDSINCOSPROGRAMSCANBEATTRIBUTEDTOPREDICTINGTHAT“BRANCHIFPLUS”WASNOTTAKENIFITHADBEENPREDICTEDASTAKEN,THEACCURACYOF
编号:201401051948096802    类型:共享资源    大小:895.62KB    格式:PDF    上传时间:2014-01-05
  
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