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Using Machine-Learning to Efficiently Explore the Architecture/Compiler Co-Design Space

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Abstract

Designingnewmicroprocessorsisatimeconsumingtask.Architectsrelyonslowsimulatorsto evaluateperformanceandasignificantproportionofthedesignspacehastobeexploredbefore animplementationischosen. Thisprocessbecomesmoretimeconsumingwhencompiler optimisationsarealsoconsidered. Oncethearchitectureisselected,anewcompilermustbe developedandtuned.Whatisneededaretechniquesthatcanspeedupthiswholeprocessand developanewoptimisingcompilerautomatically. Thisthesisproposestheuseofmachine-learningtechniquestoaddressarchitecture/compiler co-design.First,twoperformancemodelsaredevelopedandareusedtoefficientlysearchthe designspaceofamicroarchitecture.Thesemodelsaccuratelypredictperformancemetricssuch ascyclesorenergy,oratradeoffofthetwo.Thefirstmodelusesjust32simulationstomodel theentiredesignspaceofnewapplications,anorderofmagnitudefewerthanstate-of-the-art techniques. Thesecondmodeladdressesofflinetrainingcostsandpredictstheaveragebehaviourofacompletebenchmarksuite.Comparedtostate-of-the-art,itneedsfivetimesfewer trainingsimulationswhenappliedtotheSPECCPU2000andMiBenchbenchmarksuites. Next,theimpactofcompileroptimisationsonthedesignprocessisconsidered.Thishas thepotentialtochangetheshapeofthedesignspaceandimproveperformancesignificantly.A newmodelisproposedthatpredictstheperformanceobtainablebyanoptimisingcompilerfor anydesignpoint,withouthavingtobuildthecompiler.Comparedtothestate-of-the-art,this modelachievesasignificantlylowererrorrate. Finally,anewmachine-learningoptimisingcompilerispresentedthatpredictsthebest compileroptimisationsettingforanynewprogramonanynewmicroarchitecture.Itachieves anaveragespeedupof1.14xoverthedefaultbestgccoptimisationlevel.Thisrepresents61% ofthemaximumspeedupavailable,usingjustoneprofilerunoftheapplication. iii Acknowledgement

Topics: Ihadsuccessfulcollaborations, resultinginpartsofthisthesis.Thanksalsotoeveryonewho
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.184.9655
Provided by: CiteSeerX
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