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MIAOW_Architecture_Whitepaper

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MIAOWWhitepaper

HardwareDescriptionandFourResearchCaseStudies

Abstract

GPUbasedgeneralpurposecomputingisdevelopingas

aviablealternativetoCPUbasedcomputinginmanydo-

mains.Today’stoolsforGPUanalysisincludesimulatorslike

GPGPU-Sim,Multi2SimandBarra.Whileusefulformodeling

first-ordereffects,thesetoolsdonotprovideadetailedview

ofGPUmicroarchitectureandphysicaldesign.Further,as

GPGPUresearchevolves,designideasandmodificationsde-

manddetailedestimatesofimpactonoverallareaandpower.

Fueledbythisneed,weintroduceMIAOW,anopensourceRTL

implementationoftheAMDSouthernIslandsGPGPUISA,ca-

pableofrunningunmodifiedOpenCL-basedapplications.We

presentourdesignmotivatedbyourgoalstocreatearealistic,

flexible,OpenCLcompatibleGPGPUcapableofemulatinga

fullsystem.WefirstexploreifMIAOWisrealisticandthenuse

fourcasestudiestoshowthatMIAOWenablesthefollowing:

physicaldesignperspectiveto“traditional”microarchitecture,

newtypesofresearchexploration,validation/calibrationof

simulator-basedcharacterizationofhardware.Thefindings

andideasarecontributionsintheirownright,inadditionto

MIAOW’sutilityasatoolforothers’research.

1.Introduction

ThereisactiveandwidespreadongoingresearchonGPU

architectureandmorespecificallyonGPGPUarchitecture.

Toolsarenecessaryforsuchexplorations.First,wecompare

andcontrastGPUtoolswithCPUtools.

OntheCPUside,toolsspanperformancesimulators,em-

ulators,compilers,profilingtools,modelingtools,andmore

recentlyamultitudeofRTL-levelimplementationsofmicro-

processors-theseincludeOpenSPARC[39],OpenRISC[38],

IllinoisVerilogModel[56],LEON[18],andmorerecently

FabScalar[11]andPERSim[7].Inotherefforts,cleanslate

CPUdesignshavebeenbuilttodemonstrateresearchideas.

TheseRTL-levelimplementationsallowdetailedmicroarchi-

tectureexploration,understandingandquantifyingeffectsof

areaandpower,technology-drivenstudies,prototypebuilding

studiesonCPUs,exploringpower-efficientdesignideasthat

spanCADandmicroarchitecture,understandingtheeffects

oftransientfaultsonhardwarestructures,analyzingdi/dt

noise,andhardwarereliabilityanalysis.Somespecificexam-

pleresearchideasincludethefollowing:Argus[30]showed–

withaprototypeimplementationonOpenRISChowtobuild

lightweightfaultdetectors;Blueshift[19]andpowerbal-

ancedpipelines[46]considertheOpenRISCandOpenSPARC

pipelinesfornovelCAD/microarchitecturework.

OntheGPUside,anumberofperformancesimula-

tors[5,2,12,28],emulators[53,2],compilers[29,13,54],

Ultra-threadedDispatcher

CU

L2 Cache

M0CU

CU

CU

M1M2CU

CU

CU

CU

(a) GPU ArchitectureFetch Decode Schedule

LSUVector ALUInteger+FPScalar ALU

Vector General Purpose Registers (VGPR)Scalar GPRDispatcher

Memories

(b) Compute UnitHost Processor

Device

Memory

LDS

MemoryLogicIPMemory Controller

Figure1:CanonicalGPUOrganization

profilingtools[13,37],andmodelingtools[22,21,47,26]

areprevalent.HoweverRTL-levelimplementationsandlow-

leveldetailedmicroarchitecturespecificationislacking.As

discussedbyothers[10,52,42,23,32],GPUsarebeginning

toseemanyofthesametechnologyanddevice-reliabilitychal-

lengesthathavedrivensomeoftheaforementionedRTL-based

CPUresearchtopics.ThelackofanRTLlevelimplementa-

tionofaGPUhamperssimilareffortsintheGPUspace.As

CPUapproachesdonotdirectlytranslatetoGPUs,adetailed

explorationofsuchideasisrequired.Hence,wearguethatan

RTLlevelGPUframeworkwillprovidesignificantvaluein

explorationofnovelideasandisnecessaryforGPUevolution

complementingthecurrenttoolsecosystem.

Thispaperreportsonthedesign,development,character-

ization,andresearchutilityofanRTLimplementationofa

GPGPUcalledMIAOW(acronymizedandanonymizedfor

blindreviewasMany-coreIntegratedAcceleratorOfthe

Waterdeep).Figure1showsacanonicalGPGPUarchitec-

tureresemblingwhatMIAOWtargets(borrowedfromthe

AMDSIspecification,wedefineafewGPUtermsinTable3).

Specifically,wefocusonadesignthatdeliversGPUcom-

putecapabilityandignoresgraphicsfunctionality.MIAOWis

drivenbythefollowingkeygoalsandnon-goals.

GoalsTheprimarydrivinggoalsforMIAOWare:i)Re-

alism:itshouldbearealisticimplementationofaGPUre-

semblingprinciplesandimplementationtradeoffsinindustry

GPUs;ii)Flexible:itshouldbeflexibletoaccommodate

researchstudiesofvarioustypes,theexplorationofforward-

lookingideas,andformanend-to-endopensourcetool;iii)

Software-compatible:Itshouldusestandardandwidelyavail-

ablesoftwarestackslikeOpenCLorCUDAcompilerstoen-

ableexecutingvariousapplicationsandnotbetiedtoin-house

compilertechnologiesandlanguages.