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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,
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