SCaLeS 报告基础能源科学的机遇和需求

TheSCaLeSReport

OpportunitiesandNeedsinBasicEnergySciencesThomH.Dunning,Jr.JointInstituteforComputationalSciencesUniversityofTennessee•OakRidgeNationalLaboratoryOakRidge,TennesseeOutlineofPresentationBackgroundTrends:ComputingTechnologiesTrends:ScientificApplicationsScientificOpportunitiesSCaLeSWorkshopSCaLeSReportReports,Editors,andProcessRecommendationsBackground

Trends:ComputingTechnologiesComputersMicroprocessorperformancecontinuingtodoubleevery18-24months,but…increasingmismatchwithmemorysubsystemperformanceincreasingmismatchwithcommunicationssubsystemperformanceStorageDiskstoragecapacitydoublingeveryyear,but…datatransferratesincreasingonlymodestlyCommunicationsFabricIncreasingperformance,but…increasing

mismatchwithperformanceofcomputationalnodesincreasingmismatchwithneededI/OtransferratesBackground

Trends:ScientificApplicationsComputationalModelsContinuallyrefiningexistingmodelsandcreatingnewmodelsMulti-physicsandmulti-scaleproblemsposechallengesParallelComputingIncreasinguseofparallelismMostcodesscaleto10sofprocessors,afewto1-2,000processors,butalmostnoneto10,000processorsMathematicalTechniquesNewapproachesholdgreatpromiseLinearscalingreducingthegrowthrateincomputationalcostwithincreasingmoleculesizeScientificOpportunitiesCombustionScienceReactingchemicalflowsAutoignitionMolecularScienceChemicalreactivity(combustion,catalysis)Heavy-elementchemistryMaterialsScienceMaterialsdesignMultiscalematerialsmodelingNanoscienceSelf-assemblySimulationofnano-devicesSCaLeSWorkshopDate:June23-24,2003Location:Arlington,VirginiaOrganizer:D.Keyes,ColumbiaUniversityGoal:toassessthemajoropportunitiesandchallengesfacingcomputationalscienceinareasofstrategicimportancetotheOfficeofScienceParticipants:300+scientistsandengineersfromacademia,nationallaboratories,federalagenciesandotherinstitutionsSCaLeSReportEditorsDavidKeyes,Editor-in-ChiefPhilColella,LBNL(mathematics);ThomDunning,UT/ORNL(science);BillGropp,ANL(computerscience)TopicalEditorsChemistry:R.Harrison,ORNL;T.Windus,PNNLCombustion:J.Bell,LBNL;L.Rahn,SNLMaterialsScience:F.Gygi,LLNL;M.Stocks,ORNLNanoscience:P.Cummings,Vanderbilt;L-W.Wang,LBNLProcessPreliminarytopicalreportscompiledfromWorkshopnotesReportsiteratedwithWorkshopparticipantsplusothersSCaLeSReport(cont’d)TwoVolumesVolume1.SummaryandrecommendationsAvailablefordownload:/scales/Volume2.DetaileddiscussionofscientificopportunitiesandchallengesAvailableearlynextyearSciDAC:SuccessfulPrototypetoBuildOnSoftwareInfrastructureSCIENTIFICC

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SMATHEMATICSDataAnalysis&VisualizationScientificDataManagementProblem-solvingEnvironmentsProgrammingEnvironmentsCOMPUTINGSYSTEMSSOFTWAREHardwareInfrastructureSCaLeSReport

RecommendationsInvestmentsinFoundationsofComputationalModelingandSimulation#1.ComputationalScience#5.BasicTheoryandMathematicalAlgorithms#6.RecruitComputationalScientistsInvestmentsinHardwareandSoftwareInfrastructure#2.MultidisciplinaryTeams

#4.ComputingSystemsandScientificApplicationsSoftware

#3.CapabilityandCapacityComputing#8.NewComputerArchitecturesforScientificComputingInvestmentsinNetworkingandCollaborationTechnologies#7.NetworkInfrastructureandSoftwaretoSupportDistributedComputingandDataResourcesandScientificTeamsSCaLeSReport

Recommendations:FoundationsRecommendation#1

MajornewinvestmentsincomputationalscienceareneededinallofthemissionareasofDOE’sOfficeofScience,sothattheUnitedStatesisthefirst,oramongthefirst,tocapturethenewopportunitiespresentedbythecontinuingadvancesincomputingpower.Recommendation#5

Additionalinvestmentsinhardwarefacilitiesandsoftwareinfrastructureshouldbeaccompaniedbysustainedcollateralinvestmentsinalgorithmresearchandtheoreticaldevelopment.Recommendation#6

Computationalscientistsofalltypesshouldbeproactivelyrecruitedwithimprovedrewardstructuresandopportunitiesasearlyaspossibleintheeducationalprocesssothatthenumberoftrainedcomputationalscienceprofessionalsissufficienttomeetpresentandfuturedemands.InvestmentsinComputationalScience

AdvancesinMolecularSimulationsBondenergiescriticalfordescribingmanychemicalphenomenaAccuracyofcalculatedbondenergiesincreaseddramaticallyfrom1970-2000DuetoadvancesinTheoreticalmethodologyComputationaltechniquesComputingtechnologyllll1101001970198019902000Error(kcal/mol)Toachieve1kcal/molaccuracy: CCSD(T) in1989 cc-BasisSets in1989 Fastermprocessors in1990sSCaLeSReport

Recommendations:InfrastructureRecommendation#2 Multidisciplinaryteams,withcarefullyselectedleadership,shouldbeassembledtoprovidethebroadrangeofexpertiseneededtoaddresstheintellectualchallengesassociatedwithtranslatingadvancesinscience,mathematicsandcomputerscienceintosimulationsthatcantakefulladvantageofadvancedcomputers.Recommendation#4 Investmentinhardwarefacilitiesshouldbeaccompaniedbysustainedcollateralinvestmentinthesoftwareinfrastructureforthem.Theefficientuseofexpensivecomputationalfacilitiesandthedatatheyproducedependsdirectlyuponmultiplelayersofsystemssoftwareandscientificsoftwarewhich,togetherwiththehardware,aretheenginesofscientificdiscovery…DevelopingNewSimulationCapabilitiesComputerScienceAppliedMathematicsTheory(mathematicalmodel)ComputationalScience(scientificcodes)ComputationalPredictionsExperiment?YESBasicMathAlgorithmsComputerSystemsSoftwareProblemwithComputationalMethod?ProblemwithMathematicalModel?NOInadequateAdequatePerformance?NewToolforScientificDiscoverySCaLeSReport

Recommendations:InfrastructureRecommendation#3 Extensiveinvestmentsinnewcomputationalfacilitiesisstronglyrecommended,…Newfacilitiesshouldstrikeabalancebetweencapabilitycomputingforthose“heroicsimulations”thatcannotbeperformedinanyotherway,andcapacitycomputingfor“production”simulationsthatcontributetothesteadystreamofprogress.Recommendation#8 Federalinvestmentsininnovative,high-riskcomputerarchitecturesthatarewellsuitedtoscientificandengineeringsimulationsisbothappropriateandneededtocomplementcommercialresearchanddevelopment.Thecommercialcomputingmarketplaceisnolongereffectivelydrivenbytheneedsofcomputationalscience.BranscombReport

FromDesktoptoTeraflopFrontierComputersSupercomputersMid-rangeParallelComputersandClustersPersonalComputersandWorkstationsHigh-endCapacityComputingWorkgroupCapacityComputingPersonalComputingCapabilityComputingIncreasingCostperFlopIncreasingCapabilityParallelSimulations:HardvsSoftScalingSpeed-upNumberofProcessors“Hard”Scaling– nearlinearspeed-upindependentofproblemsize– uncommon“Soft”Scaling– decreasingspeed-upwithconstantproblemsize– increaseproblemsizetomaintainscaling

butcostofcalculationcanincreasemorerapidlythanthatgainedfromincreasedscalability– commonincreasingproblemsizeSCaLeSReport

Recommendations:NetworksandCollabsRecommendation#7

Sustainedinvestmentsmustbemadeinnetworkinfrastructureforaccessandresourcesharing,