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EXPLOITINGCHOICEINSTRUCTIONFETCHANDISSUEONANIMPLEMENTABLESIMULTANEOUSMULTITHREADINGPROCESSORDEANMTULLSEN,SUSANJEGGERS,JOELSEMERY,HENRYMLEVY,JACKLLO,ANDREBECCALSTAMMYDEPTOFCOMPUTERSCIENCEANDENGINEERINGYDIGITALEQUIPMENTCORPORATIONUNIVERSITYOFWASHINGTONHLO23/J3BOX35235077REEDROADSEATTLE,WA981952350HUDSON,MA01749ABSTRACTSIMULTANEOUSMULTITHREADINGISATECHNIQUETHATPERMITSMULTIPLEINDEPENDENTTHREADSTOISSUEMULTIPLEINSTRUCTIONSEACHCYCLEINPREVIOUSWORKWEDEMONSTRATEDTHEPERFORMANCEPOTENTIALOFSIMULTANEOUSMULTITHREADING,BASEDONASOMEWHATIDEALIZEDMODELINTHISPAPERWESHOWTHATTHETHROUGHPUTGAINSFROMSIMULTANEOUSMULTITHREADINGCANBEACHIEVEDWITHOUTEXTENSIVECHANGESTOACONVENTIONALWIDEISSUESUPERSCALAR,EITHERINHARDWARESTRUCTURESORSIZESWEPRESENTANARCHITECTUREFORSIMULTANEOUSMULTITHREADINGTHATACHIEVESTHREEGOALS1ITMINIMIZESTHEARCHITECTURALIMPACTONTHECONVENTIONALSUPERSCALARDESIGN,2ITHASMINIMALPERFORMANCEIMPACTONASINGLETHREADEXECUTINGALONE,AND3ITACHIEVESSIGNIFICANTTHROUGHPUTGAINSWHENRUNNINGMULTIPLETHREADSOURSIMULTANEOUSMULTITHREADINGARCHITECTUREACHIEVESATHROUGHPUTOF54INSTRUCTIONSPERCYCLE,A25FOLDIMPROVEMENTOVERANUNMODIFIEDSUPERSCALARWITHSIMILARHARDWARERESOURCESTHISSPEEDUPISENHANCEDBYANADVANTAGEOFMULTITHREADINGPREVIOUSLYUNEXPLOITEDINOTHERARCHITECTURESTHEABILITYTOFAVORFORFETCHANDISSUETHOSETHREADSMOSTEFFICIENTLYUSINGTHEPROCESSOREACHCYCLE,THEREBYPROVIDINGTHE“BEST”INSTRUCTIONSTOTHEPROCESSOR1INTRODUCTIONSIMULTANEOUSMULTITHREADINGSMTISATECHNIQUETHATPERMITSMULTIPLEINDEPENDENTTHREADSTOISSUEMULTIPLEINSTRUCTIONSEACHCYCLETOASUPERSCALARPROCESSOR’SFUNCTIONALUNITSSMTCOMBINESTHEMULTIPLEINSTRUCTIONISSUEFEATURESOFMODERNSUPERSCALARSWITHTHELATENCYHIDINGABILITYOFMULTITHREADEDARCHITECTURESUNLIKECONVENTIONALMULTITHREADEDARCHITECTURES1,2,15,23,WHICHDEPENDONFASTCONTEXTSWITCHINGTOSHAREPROCESSOREXECUTIONRESOURCES,ALLHARDWARECONTEXTSINANSMTPROCESSORAREACTIVESIMULTANEOUSLY,COMPETINGEACHCYCLEFORALLAVAILABLERESOURCESTHISDYNAMICSHARINGOFTHEFUNCTIONALUNITSALLOWSSIMULTANEOUSMULTITHREADINGTOSUBSTANTIALLYINCREASETHROUGHPUT,ATTACKINGTHETWOMAJORIMPEDIMENTSTOPROCESSORUTILIZATIONLONGLATENCIESANDLIMITEDPERTHREADPARALLELISMTULLSEN,ETAL,27SHOWEDTHEPOTENTIALOFPROCEEDINGSOFTHE23RDANNUALINTERNATIONALSYMPOSIUMONCOMPUTERARCHITECTURE,PHILADELPHIA,PA,MAY,1996ANSMTPROCESSORTOACHIEVESIGNIFICANTLYHIGHERTHROUGHPUTTHANEITHERAWIDESUPERSCALARORAMULTITHREADEDPROCESSORTHATPAPERALSODEMONSTRATEDTHEADVANTAGESOFSIMULTANEOUSMULTITHREADINGOVERMULTIPLEPROCESSORSONASINGLECHIP,DUETOSMT’SABILITYTODYNAMICALLYASSIGNEXECUTIONRESOURCESWHERENEEDEDEACHCYCLETHOSERESULTSSHOWEDSMT’SPOTENTIALBASEDONASOMEWHATIDEALIZEDMODELTHISPAPEREXTENDSTHATWORKINFOURSIGNIFICANTWAYSFIRST,WEDEMONSTRATETHATTHETHROUGHPUTGAINSOFSIMULTANEOUSMULTITHREADINGAREPOSSIBLEWITHOUTEXTENSIVECHANGESTOACONVENTIONAL,WIDEISSUESUPERSCALARPROCESSORWEPROPOSEANARCHITECTURETHATISMORECOMPREHENSIVE,REALISTIC,ANDHEAVILYLEVERAGEDOFFEXISTINGSUPERSCALARTECHNOLOGYOURSIMULATIONSSHOWTHATAMINIMALIMPLEMENTATIONOFSIMULTANEOUSMULTITHREADINGACHIEVESTHROUGHPUT18TIMESTHATOFTHEUNMODIFIEDSUPERSCALAR；SMALLTUNINGOFTHISARCHITECTUREINCREASESTHATGAINTO25REACHINGTHROUGHPUTASHIGHAS54INSTRUCTIONSPERCYCLESECOND,WESHOWTHATSMTNEEDNOTCOMPROMISESINGLETHREADPERFORMANCETHIRD,WEUSEOURMOREDETAILEDARCHITECTURALMODELTOANALYZEANDRELIEVEBOTTLENECKSTHATDIDNOTEXISTINTHEMOREIDEALIZEDMODELFOURTH,WESHOWHOWSIMULTANEOUSMULTITHREADINGCREATESANADVANTAGEPREVIOUSLYUNEXPLOITABLEINOTHERARCHITECTURESNAMELY,THEABILITYTOCHOOSETHE“BEST”INSTRUCTIONS,FROMALLTHREADS,FORBOTHFETCHANDISSUEEACHCYCLEBYFAVORINGTHETHREADSMOSTEFFICIENTLYUSINGTHEPROCESSOR,WECANBOOSTTHETHROUGHPUTOFOURLIMITEDRESOURCESWEPRESENTSEVERALSIMPLEHEURISTICSFORTHISSELECTIONPROCESS,ANDDEMONSTRATEHOWSUCHHEURISTICS,WHENAPPLIEDTOTHEFETCHMECHANISM,CANINCREASETHROUGHPUTBYASMUCHAS37THISPAPERISORGANIZEDASFOLLOWSSECTION2PRESENTSOURBASELINESIMULTANEOUSMULTITHREADINGARCHITECTURE,COMPARINGITWITHEXISTINGSUPERSCALARTECHNOLOGYSECTION3DESCRIBESOURSIMULATORANDOURWORKLOAD,ANDSECTION4SHOWSTHEPERFORMANCEOFTHEBASELINEARCHITECTUREINSECTION5,WEEXAMINETHEINSTRUCTIONFETCHPROCESS,PRESENTSEVERALHEURISTICSFORIMPROVINGITBASEDONINTELLIGENTINSTRUCTIONSELECTION,ANDGIVEPERFORMANCERESULTSTODIFFERENTIATETHOSEHEURISTICSSECTION6EXAMINESTHEINSTRUCTIONISSUEPROCESSINASIMILARWAYWETHENUSETHEBESTDESIGNSCHOSENFROMOURFETCHANDISSUESTUDIESINSECTION7ASABASISTODISCOVERBOTTLENECKSFORFURTHERPERFORMANCEIMPROVEMENTWEDISCUSSRELATEDWORKINSECTION8ANDSUMMARIZEOURRESULTSINSECTION9THISRESEARCHWASSUPPORTEDBYONRGRANTSN0001492J1395ANDN000149411136,NSFGRANTSCCR9200832ANDCDA9123308,NSFPYIAWARDMIP9058439,THEWASHINGTONTECHNOLOGYCENTER,DIGITALEQUIPMENTCORPORATION,ANDFELLOWSHIPSFROMINTELANDTHECOMPUTERMEASUREMENTGROUPINSTRUCTIONCACHE8DECODEREGISTERRENAMINGFLOATINGPOINTINSTRUCTIONQUEUEINTEGERINSTRUCTIONQUEUEFPUNITSINT/LDSTOREUNITSDATACACHEPCFETCHUNITINTEGERREGISTERSFPREGISTERSFIGURE1OURBASESIMULTANEOUSMULTITHREADINGHARDWAREARCHITECTURE2ASIMULTANEOUSMULTITHREADINGPROCESSORARCHITECTUREINTHISSECTIONWEPRESENTTHEARCHITECTUREOFOURSIMULTANEOUSMULTITHREADINGPROCESSORWESHOWTHATTHETHROUGHPUTGAINSPROVIDEDBYSIMULTANEOUSMULTITHREADINGAREP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