GCS HLRS · has become a major research hub for simulation in Europe. The focus of our work is on...

GCS HLRS

•MichaelResch,

Universityof Stuttgart,HLRS

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researchworkforapplicationsonsupercomputers.

OverthelastyearsHLRShasexpandeditsresearchactivitiessubstantiallyandhasbecomeamajorresearchhubforsimulationinEurope.ThefocusofourworkisondevelopingnewmethodsandapplicationsinthefieldofHPCanddistributedcomputing.GridandCloudcomputingareonlytwoofthecornerstonesofourresearch.Visualizationisamainfieldofourresearchasitenablestheintegrationofsimulationinworkflowsbothinscienceandindustry.ThefundingfortheseprojectscomesfromtheStateofBaden-Württemberg,theFederalMinistryofScience(BMBF),theDeutscheForschungsgemein-schaft(DFG),theEuropeanCommis-sionanddirectlyfromindustry.

Abookletlikethiscanonlygivearoughoverviewofouractivities.Oursystemsareupandrunning7x24.SupportforourusersisgivenfromBarcelonatoPoznanandfromRometoStockholm.Ontheaverage80usergroupsacrossEuropehaveaccesstooursystems.Eachofthesegroupsisfocusingonresearchprojectsinthefieldofsimulation.Atthesametimemorethan30inhouseresearchprojectsinthefieldofHPCareconductedbyscientistsfromHLRS.TogethertheseactivitiescreateacenterofexcellencethatisacornerstoneoftheUniversityofStuttgartandisrenownedworldwide.

HighPerformanceComputingattheUniversityofStuttgartstartedabout50yearsago.In1996theHPCCenter/HöchstleistungsrechenzentrumStuttgart(HLRS)wasfoundedasthefirstGermannationalHPCcentre.SincethenHLRShasestablisheditselfasoneofthelead-ingcentersworldwidewithitsfocusonengineeringscienceandindustrialHPC.FromthestartHLRSwasgivingsupporttolocalindustrialleaderslikeDaimlerandPorsche.ThroughhwwGmbHHLRSprovidesindustrywithaccesstosystemsandthroughtheAutomotiveSimulationCenterStuttgart(ASCS)providesexpertiseinsimulation.Since2007HLRSisamemberintheGaussCentreforSupercomputing(GCS).TogetherwithitsfellowcentresatJülichandGarchingitsupportsEuropeanresearch-ersintheEuropeanPartnershipforAdvancedComputinginEurope(PRACE).Throughalongtermstrategichard-wareandsoftwareconceptHLRShaslaidthefoundationforthesuccessfulusageofsimulationscienceinresearchandindustry.

ThemissionofHLRStodayistoactasacentreofcompetence,supportusersandconductresearchinthefieldofHPC.ThefocusofourusersisonCom-putationalFluidDynamics.Applicationsofthistechnologyrangefromflowoverthewingsofanaircrafttotheoceancirculation.Inthisbookletwepresentthehighlightsofapplicationsoftheyears2010and2011.ManyofthemwereawardedtheGoldenSpikeAwardofthesteeringcommitteeofHLRS.Thisawardisgiveneveryyeartoyoungsci-entistswhoshowoutstandingqualityof

Welcome to the World of HLRS

HLRS» GrowingScienceatHLRS–BeyondBareMetal» Industryinside» ComputingSystems

Applications» Laminar Flow Control in Vortex-deformed Swept-WingFlows:PinpointSuction» Fluid-Structure coupled Flow Simulations of Helicopter Rotors» Direct Numerical Simulation of Active Separation Control Devices» High Order large Scale Calculations» Exotic State in Correlated Relativistic Electrons» The Agulhas System as a Key Region of the global oceanic Circulation» Modelling Convection over West Africa» The Maturing of Giant Galaxies by Black Hole Activity

Projects» CoolEmAll-PlatformforOptimizingtheDesign, OperationandCoolingofmodularconfigurable ITInfrastructures» VISIONAIR » Collaborative Research into Exascale Systemware, Tools and Applications (CRESTA)» Debugging on the next Level: Temanejo» Towards High Performance Semantic Web – Experience of the LarKC Project» plugIT – Plug Your Business into IT» GAMES (Green Active Management of Energy in IT Service centres)» Towards EXascale ApplicatTions (TEXT)

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Figure1:ProjectfundingofHLRSoverthelast10years Figure2:NumberofHLRSemployeesoverthelast7years(RAisstudentresearchassistants

Theimportanceofasupercomputingcenteristypicallymeasuredinspeedofitssupercomputer.Asoftodaythekeynumbertobeachievedis1Petaflop/sandbeyond.However,thesystemitselfisbyfarnotenough.Itisonlyatooltodevelopsolutions.Hence,systemshavetobesetinaworking,stable,andwellorganizedenvironment.Firstofall,theyneedanexcellentinfrastructuretohostthem.Second,theyneedsupportbythehostingcenterthathelpsuserstogetperformancefromthesystem.Third,ittakesalotofsystemrelatedresearchtodevelopnewmethodsandharvestthefullpotentialofasuper-computer.Finally,inordertobridgethegapbetweenpureresearchandrealworldusageofthesystems,aconceptisrequiredtointegratesupercomputingintotherealworldofresearch,devel-opmentandproduction.

HLRShasinstalledaCrayXE6systemrecently.Thepeakperformanceisintherangeof1PF/sandweseesustainedperformanceintheorderof20-25%ofthepeakperformance[1].BeyondthepurehardwareHLRShasmadefurtherstepstogrowitsHPC-ecosystem.

InfrastructurePowersupplyandcoolingareoneofthemostpressingissuesthesedays.ThereforeHLRShasbuiltnewpowerandcoolingfacilities.Theyprovideadditional4MWofpowerwhichwillbringpowersupplyofHLRStoatotalof5MW.Atthesametimeitwillprovideaveryefficientwatercoolinginfra-structurerelyingfullyonfreecoolingupto18degreesCelsiusoutsidetemperature.AnaveragePUEof1,15isexpected.EnergyoftheCrayXE6willbeusedinthenewresearchbuildingofHLRScurrentlyunderconstruction.

Withanincreaseinnumberofresearchprojectsandfunding(seeFig.1)alsothenumberofscientistshasgrown(seeFig.2)overthelastyears.InordertomeettherequirementsofHLRS,theplanningforanewresearchbuildingwasstartedin2007andtheconstructionworkstartedinJune2011.ThebuildingisanextensionoftheexistingHLRSheadquarterandwillprovideadditional1,950m²ofof-ficespaceallowingHLRStobringallemployeestogetherinasingleenviron-ment.Furthermorethenewbuilding

Growing Science at HLRS – Beyond Bare Metal

Activities

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•MichaelResch,


willintegrateafive-sidedVirtualRealityenvironment.Ontheonehandthiswillallowformorerealisticvisualizationofsimulationresults.OntheotherhandthenewCAVEwillhaveadirectphysi-callinktothesupercomputersallowingmoreinteractiveusagemodelssuchassimulationsteering.

ResearchOverthelastyearsHLRShasincreasedthescopeofitsresearchtremendously.FourmajorlevelsofresearchcanbeidentifiedwhicharemergedbyHLRSintoacoherentprogramofresearchforHighPerformanceComputing.

Statelevel:AtthestatelevelHLRShasagreedwiththeStateMinistryofScienceonamajorinitiativetodevelopscalablesoftwareforHighPerformanceComputing.Theinitiativewillmakeavailableupto30Mio.Euroforthepurchaseanddevelopmentofhighlyscalablesoftware.

Federallevel:SincethestartHLRSisparticipatingintheHighPerformanceComputingsoftwareinitiativeoftheGermanFederalMinistryofScience.HLRSisworkingonavarietyofapplicationdrivenresearchprojectsthataimattransformingflopsintosolutions.AtthesametimeHLRSistakingpartinaGermanClusterofExcellencefundedbytheGermanResearchSociety(DFG)called“SimTech”[2].SimTechhasafocusonbasicmethodsinsimulationtechnologyandistheonlysuchclusterofexcellenceinGermany.WithinSim-TechtheDirectorofHLRSProfessorMichaelReschservesasaprincipalinvestigatorforHPC.

Europeanlevel:AttheEuropeanlevelHLRShasalonghistoryofprojectresearchandcollaboration.Themost

recentHighPerformanceComputingprojectCRESTAisaimingatsupportingthedevelopmentofExascalesystemsinEurope.However,projectresearchatHLRSgoesbeyondExascale.ManyofourprojectsaimatmakingHPCmoreproductiveorbringingHPCclosertotheindustrialenvironmenttowhichHLRSisconnected.

Industriallevel:ThecollaborationofHLRSwithindustryismanifold.HLRShaslongcollaboratedwithMicrosoftindevelopingsoftwareforclustersystems.RecentlyHLRSstartedaCrayCenterofDevelopment.OverthenextfiveyearsresearchersfromCrayandfromHLRSwillworkonscalableapplicationsandontoolstoharvestthepotentialoffutureHPCsystems.

SummaryBeyondtheinstallationofhardwareanditsparticipationinorganizationalcollaborationslikeGCSandinfrastructureinitiativeslikePRACE[3]HLRSisheavilyinvolvedinresearch.OverthelastyearsandintheyearstocomeHLRSissubstantiallyimprovingitsinfrastructureandhasatthesametimesetupaframe-workforresearchthatintegratesvariousscopesandlevelsoffundingtodevelopbettersolutionsforitsshare-holders.

References[1] Resch,M. NewHLRSSystemHERMIT,inSiDE, Vol.9,No.1,Spring2011

[2] SimtechExcellenceCluster, www.simtech.uni-stuttgart.de

[3] PRACE,www.prace-ri.eu

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HighPerformanceComputingiswidelyclaimedtohaveanimportantimpactontheeconomyofacountry.HPCisconsideredtobeakeytechnologyinordertostaycompetitive(seeforex-ample[1]).However,alookatthefast-estsystemsintheworldsrevealsthatstill92%ofthetop100systemsarenotindustrialsystems.Thisdiscrepancyisstriking.OnewouldexpectindustrytouseHPCasmuchaspossibleinordertoimproveitscompetitiveness.InthisarticlewedescribethemodelsuccessfullydeployedatHLRSachievingHPCintegrationinthesimulationprocessesinindustryonlargescalecomputingfacilitiesalreadyformanyyears.Furthermore,wepresentnewapproachesofhowtoextendthereachofHPCbothintypeofcompany(SME)andintypeofusage.

RoadblocksSo,whataretheroadblocksforindustrialHPCmakingitobviouslysodifficulttouseHPCinindustry?First,therearecostissues.HPCisexpensive.Whenlookingatthetop100systemsintheworldwehavetoconsiderinvestmentcostsintherangeofEuro5Mio.,andadditionalfixedoperationalcostsinasimilarrangeoveranoperationalperiodof3years.Furthermoretheincreasedpowerdemandandcorre-spondingneedforaninfrastructurewithahighlevelofpowerefficiencyleadtosubstantiallyraisedinvestmentcostsfortheinfrastructure.Foratop100systemonewouldestimatethistobeintheorderofanotherEuro5to10Mio.atleastevery5years.AllinallHPCrequiresarelativelyhighleveloffinancialeffortpotentiallydeliveringbenefitinthelongtermbuttypicallyHPCdoes

notguaranteeimmediatereturnofinvestment.AdditionallycostsforHPCinvestmentarefixedcosts.Theycanhardlybereducedintimesofcrisis.

TheobviousalternativeisoutsourcingofHPC.However,publicHPCsystemsoperateinawell-definedenvironment-whichtypicallyisexactlytheoppositeofwhatindustryrequires.ThefocusofapublicHPCcenterisonopenness(wecansafelyignorethespecialcasesofclassifiedsystemsasthese-bynature-arenotavailabletoindustry).Theserviceprovidedisbesteffortastypicallytheservicesareprovidedforfreeorbasedonaresearchgrant.Forindustryopennessisnotaproblempersebutveryoftensimulationdataandsoftwarearesensitive.So,wayshavetobefoundtoguaranteedatasecurityandsafety.Besteffortwasacceptable10to15yearsagowhensimulationinindustrywasitselfaresearchactivityoranaccompanyingmeasureatbest.Today,assimulationisonetooltodevelopnewproducts,andexperimentsareoftenmademainlytosupportresultsofextensivesimulations,simulationisacorepartofwhatindustrycallsproduction.Andasmuchasindustryisworriedaboutstoppingphysicalproductionlines,italsostartstoworryaboutitsvirtualproductionlines-ascanbefoundinHPCsimulation.

PublicHPCforIndustryHPCattheUniversityofStuttgarthasalongtradition.Atleastsince25yearsthecomputingcenterhasoperatedsystemstogetherwithindustry.Com-moninvestmentsweremadealreadybackin1986.Hence,whentheHighPerformanceComputingCenterStuttgart(HLRS)wasfoundedin1996asthefirstGermannationalsupercomputingcenter,anindustrialstrategywasan

Industry inside

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importantpartoftheconcept.Inthelast15yearsthisconcepthasbeenextended.GeographicallythiswasachievedbysettingupverycloserelationswiththeKarlsruheInstituteofTechnology(KIT)anditsSteinbuchCenterforComputing(SCC).Organizationwisethisisamendedbycontinuouslyworkingwithourindustrialpartnersfurtherdevelopingthenecessarytoolsandorganizationalframeworks.NotonlyprovidingHPCcyclestoindustrybutbeyondthatshapingtheprocessesandmethodsrequiredforindustrialHPCadoption.In2011thisconcepthasbeenmovedtoanewlevelintroducingacoupleofdeepchangestotheexistingco-operationmodels.ChangesthatareextremelyimportantforHLRSbutthatcanalsoserveasamodelforageneralconceptofindustrialHPCusageworld-wide.

AnindustrialSolutionIndustrialuseofHPCsystemscomesindifferentflavorsthatrequiredifferentapproachestomeettheirindividualneeds.Thereisnoonesizefitsallapproachforthis.Inthefollowingwelookatthreekeyconceptsthathavedrawnsomeattentioninthelastyears:• Simulationproductioninindustry• Improvementofindustrialin-house methods• Pre-competitiveresearch.

SimulationProductionSimulationasdescribedaboveispartofevery-daydesignanddevelopmentprocessinmanycompaniesalready.Assuchsimulationispartofprocesschainsandhastobeintegratedintooverallprocesses.HPCneedstobemadeavailableonareliableandsecurebasis.Noeffortsforresearcharerequired.

HLRSsetupasolutionforthistogetherwithindustrialpartnersalreadyin1995.

TheHöchstleistungsrechnerfürWissen-schaftundWirtschaftBetriebsgesell-schaftmbH(hww)wasestablishedtogetherwithDaimlerandPorsche.OvertimeitsownershipchangedfromDaimlertoT-Systemswhichiscurrentlyholding40%ofhww.Porscheisstillholding10%.OvertimeHLRSinvitedfurtheruniversitiestojointheminhwwsuchthattoday50%ofthesharesofhwwareevenlysplitbetweentwouniversitiesandtheStateofBaden-Württemberg.

WithHPCbecomingmoreandmorepartoftheproductionprocesstheoperationalmodelofhwwhadtobechangedtoo.Overthelast3yearstheadvisoryboardofhwwhasworkedoutanewbusinessconcept.AccordingtothisHighPerformanceComputingisnowprovidedasacommodityviahww.hwwservesasaplatformandcanpro-videaccesstovariousHPCsystemsondemandinacloud-likeway.ThesuccessofmovingtosuchaserviceorientedapproachcaneasilybeseenfromtheusageofHPCsystemsatHLRS.Overthelastyearsusagehasincreasedfromroughly2millioncorehoursin2007toanewrecordofover20Mio.corehoursin2011.Currentusagenumbersanddiscussionswithindustryindicatethatthegrowthwillcontinueoverthecomingtwoyears.

Thesuccessofhwwisbasedonanumberoffactors:• Cleartechnicalconceptforoperationincludingsecuritymeasuresconformingtoindustrialstandardsandoperationalproceduresagreeduponbetweenindustryandpubicproviders.

• Clearfinancialconceptmakingsurethatbothpublicrequirementsandeconomicnecessitiesarecombinedtofindanoptimumeconomicsolution.

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• Clearlegalproceduresthatensurethatalllegalregulationsaremetandthattaxissuesareresolvedinsuchawayastoeliminatethependingrisksoftaxationforthepublicsector.

Eventhoughsharingpublicresourceswithindustryimposesnewrulesandadditionalworkonthepublicsideitalsocomeswithanumberofbenefits.• ClarificationoffinancialaspectsofHPC.AcomparisonofpublicHPCser-viceswithprivatecloudofferingsiseasilypossibleandcanbedoneanytime.• Increasedlevelofoperationalsecuritywhichisalsobeneficialforthepublicusers.• StabilizationoffinancialplanningasindustrialusagegivespoliticalbackingforHPCinvestment.

Improvingin-houseMethodsImprovementofin-housemethodsisoftendiscussedinthedebateaboutprivateuseofpublicHPCresources.TheargumentgoesthatindustryneedstohaveaccesstoHPCinordertobeableco-operatewithpublicresearchtoimproveitsprocesses.InGermanythereisalongtraditionforindustrial-universityco-operationthathascreatedaframeworkthatisuniqueworld-wide.Inthefieldsofengineering,chemistry,electronicsandmanyotherappliedsciencesresearchdepartmentsofuniversitiesandresearchorganizations

typicallyworktogetherwithindustryinresearchprojects.Veryoftensuchcollaborationshavebeenestablished50ormoreyearsagoandarehandedoverfromdirectortodirectorofaninstituteovertime.Aspartofsuchwell-establishedframeworkspublicandprivateresearcharewellconnected.Currentlyweestimatethataboutonethirdofallprojectsrunningonthesys-temsofHLRShavesomerelationwithapublicindustrialresearchproject.Theverypositivethingaboutsuchprojectsisthattheresultsachievedaremadepubliclyavailable.Onlyiftheresearchisdoneprivatelyandresultsarekeptconfidential,industryhastogothroughthehwwmodelasdescribedabove.

AnotheradvantageofthismodelisthatmanymasterthesesandPhDthesesaredoneincollaborationbetweenpublicresearchdepartmentsandindustry.Throughthisprocessalotofknow-howiscreatedonbothsidesthatbenefitsbothsides.Youngresearchersgetafirstlookintoindustrialprocessesduringtheirthesis.Industrygetstoworkwiththemostadvancedmethodsandcaneasilyrecruitwelltrainedstaffthroughsuchco-operations.

Pre-competitiveResearchWhenitcomestointernationalcompetitivenessanationalapproachisimportant.But,ontheotherhand,competitionisnotonlyaninternationalissuebutalsoanationalone.So,onehastofindawaytotargetgeneralre-searchtopicsbygettingthebestresearcherstofocustheirmindonthem.Thatmaydefinethecompetitivenessofanationaleconomyinthedecadestocome.Andatthesametimeonehastofindawaytokeepindustrialcompaniesclosetosuchresearchinordertoallowaneasyandquicktransitionofresearchresultsintotheindustrial

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•MichaelResch,


simulationprocess.HLRSandSCChavedecidedtosetup“SolutionCenters”inordertomeetbothrequirements.Todaythefollowingareactive:• AutomotiveSimulationCenter Stuttgart(ASCS)• EnergySolutionCenterKarlsruhe (ENSOC).Bothcentersbringtogetherresearchersandindustry.BothcentershaveintegratedHWandSW-vendorstomakesurethatwhatevernewmethodsareworkedoutbyresearcherscanimmediatelybeintegratedintoexistingcommercialsoftwareandcanbeoptimizedforexistinghardwarefast.

BeyondlargeScaleIndustries–SME-to-HPCUsageofHPCrequiresahighlevelofexpertise.Furthermoreitcomeswithsomecosts.Finallythereisatrustissueconcerningindustrialdata.That’swhySMEstypicallyarenotatthefore-frontofpublicHPCusage.Forthemallthreeissuesareextremelydifficulttohandle.ExpertiseforHPCistypicallynotpartofthecorebusinessofsmallercompanies.TheavailablebudgetforHPCusageistightandevensmallinitialinvestmentsmaybeprohibitivelyhigh.Finally,knowledgeanddataareveryoftenthekeyandonlyassetsofasmallcompany.

HLRSandSCCarethereforespecificallytargetingSMEsintheStateofBaden-Württemberg.InBaden-Württembergalotofinnovationisdeliveredbythesesmallcompanies.Anysupportforthemmaycreateasubstantiallocalreturnofinvestment.SupportforSMEsreliesontrust.Hence,thefocushastobeonlocalinitiatives.Trustfurthermorerequiresalotofpersonalinvolvement.Especiallyatthebeginningitisimportanttohavepeopleathandthatlistenandprovidesolutionsifproblemsoccur.HLRSandSCCthereforedecidedtoset

upanewcompany.Followingtheacro-nymofhww(providingpurecloudlikeHPCcomputeservices)thenewcompanywasnamedHöchstleistungs-rechnerundVerteiltesRechnenVerbund(HVV).OneofthekeyactivitiesofHVVwillbetoidentifySMEsinBaden-WürttembergthatmaybenefitfromHPC,toeducatethemonthebenefitsofHPC,toproposeprojects,andtosupporttheminintegratingHPCintotheirownproductionschemes.HVVwillhavetoworkcloselywithHLRSandSCCtobesuccessful.However,weexpecttoseeatremendousimpactonHPCusageinSMEsoverthenext5yearsiftheconceptworksout.

ConclusionSupportforindustryinHPCisadifficulttaskforanypubliccenter.However,experienceshowsthatonecanbeverysuccessfulandcompetitivefocusingontherightissues.ThesewhereresolvedsuccessfullyintheStateofBaden-Württembergandthefindingscanbeusefulbeyond.Goingbeyondpurecycleprovisioningspeedofinnovationisaddressedbysolutioncenters.BasedonHVVweaddresstheSME-sphere.Bothactivitieswillrequiremoreeffortsinthecomingyearsandareexpectedtodeliverahighreturnofinvestmentforsocietyandeconomy.BothapproachesshouldbeextendedbeyondtheStateofBaden-Württemberg.

References[1] Joseph,EarlC.etal. AStrategicAgendaforEuropean LeadershipinSupercomputing:HPC2020- IDCFinalReportoftheHPCStudyforthe DGInformationSocietyoftheEuropean Commission,http://www.hpcuserforum. com/EU/downloads/SR03S10.15.2010.pdf,

lastaccessedAugust14,2011

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ViewoftheHLRSCrayXE6"HERMIT"

FirstGermanNationalCenterBasedonalongtraditioninsupercom-putingatUniversityofStuttgart,HLRS(HöchstleistungsrechenzentrumStuttgart)wasfoundedin1995asthefirstGermanfederalCentreforHighPerformanceComputing.HLRSservesresearchersatuniversitiesandresearchlaboratoriesinEuropeandGermanyandtheirexter-nalandindustrialpartnerswithhigh-endcomputingpowerforengineeringandscientificapplications.

ServiceforIndustryServiceprovisioningforindustryisdonetogetherwithT-Systems,T-Systemssfr,andPorscheinthepublic-privatejointventurehww(HöchstleistungsrechnerfürWissenschaftundWirtschaft).Throughthisco-operationindustryalwayshasaccestothemostrecentHPCtechnology.

BundlingCompetenciesInordertobundleserviceresourcesinthestateofBaden-WürttembergHLRS

hasteamedupwiththeSteinbuchCenterforComputingoftheKarlsruheInstituteofTechnology.Thiscollaborationhasbeenimplementedinthenon-profitorganizationHVV.

WorldClassResearchAsoneofthelargestresearchcentersforHPCHLRStakesaleadingroleinresearch.ParticipationintheGermannationalinitiativeofexcellencemakesHLRSanoutstandingplaceinthefield.

Contact:HöchstleistungsrechenzentrumStuttgart(HLRS)UniversitätStuttgart

Prof.Dr.-Ing.Dr.hc.Dr.h.c.MichaelM.ReschNobelstraße1970569StuttgartGermany

[email protected]/www.hlrs.de

Computing Systems@HLRS

Centres

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ViewoftheHLRSBW-GridIBMCluster(Photo:HLRS)

ComputeserverscurrentlyoperatedbyHLRS

AdetaileddescriptioncanbefoundonHLRS’swebpages:www.hlrs.de/systems

System Size

PeakPerformance

(TFlop/s) PurposeUserCommunity

CrayXE6"HERMIT"(Q42011)

3,552dualsocketnodeswith113,664AMDInterlagoscores

1,045 CapabilityComputing

EuropeanandGermanResearchOrganizationsandIndustry

NECHybridArchitecture

1216-waynodesSX-9with8TBytemainmemory+5,600IntelNehalemcores9TBmemoryand64NVIDIATeslaS1070

146 CapabilityComputing

GermanUniversities,ResearchInstitutesandIndustry,D-Grid

IBMBW-Grid 3,984IntelHarpertowncores8TBytememory

45.9 GridComputing

D-GridCommunity

CrayXT5m 896AMDShanghaicores1.8TBytememory

9 TechnicalComputing

BWUsersandIndustry

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HLRShasalongtraditioninsupportingusersonHPCsystems.IntegratedinoneoftheleadingGermantechnicaluniversities–theUniversityofStuttgart–HLRSdrawsontheexpertiseofapplicationexpertsinmechanicalengineering,physicsandchemistryaswellasontheknow-howofcomputerscientistsandmathematicians.ItisonlynaturalthatwithintheGaussCentreforSupercomputing(GCS)HLRStakesaleadingroleforengineeringapplicationsandindustrialusageofHPC.However,usageofHLRSsystemsgoesbeyond.Inthefollowingwepresentthehigh-

lightsofapplicationsofthelasttwoyears.ManyofthemwereawardedtheGoldenSpikeAwardofthesteeringcommitteeofHLRS.Thisawardisgiveneveryyeartoyoungscientistswhoshowoutstandingqualityofresearchworkforapplicationsonsupercomputers.

ThebiggestshareinusageofHLRSsystemsisforapplicationsusingCom-putationalFluidDynamics.Applicationsofthistechnologyrangefromflowsinmechanicalengineeringtooceancirculationtoastrophysicalapplications.InthisbookletyouwillfindanumberofexamplesfortheapplicationofCFDinturbulenceresearch.Turbulenceisoneofthebigchallengesinengineering.Turbulentflowsincreasetransportationcostandmakesystemslessmanageable.

Averygoodexamplefortheapplicationofturbulenceresearchistheinvestigationofturbulenceoverthewingofanaircraft.Thereductionofturbulenceresultsinareductionoffuelconsumptioninairtraffic.ResearchersoftheInstitutefor

User App licationsUser Applications

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•MichaelResch,


Aero-andGasdynamicsoftheUniversityofStuttgartwereabletodevelopanewwingshapethatwillhelptoreducefuelcostsforairplanesbyabout15%.ThiswillresultnotonlyinareductionoffuelcostsforairlinesbutalsoinasubstantialreductionofCO²emissions.

Attheotherendofthespectrumwefindlargescalesimulationsofoceancirculationthatisessentialtobetterunderstandtheinteractionsbetweenoceanandatmosphereinclimateresearch.Suchlargescalesimulationsrelyheavilyonthelargememoryofsupercomputersandonthehighlevelofperformance.Largememoryisessentialtoadequatelyresolvethefeaturesofalargeregion.Highperfor-manceisrequiredtobeabletostudylongtermphenomenathatarecrucialfortheworldclimate.

Furtherapplicationspresentedheredealwithphenomenainphysics.Againwepresenttwosidesofthespectrum.Ontheoneendwefindthesimulationofatoms.Suchsimulationsareneces-

sarytobetterunderstandthebehaviorofmaterials.Withincreasingmemorysizeandperformancenextgenerationsystemsareabletosimulatelargernumbersofatomsandhenceincreaseourunderstandingofcomplexmaterialbehavior.Attheotherendofthespec-trumwefindastrophysicalsimulations.Theunderstandingoftheprocessesthatactintheuniversecanbeusefultobettergraspthegenesisoftheworldasweknowittoday.

TheexamplespresentedherewerechosenfromourjournalInSiDEwhichispublishedtwiceayear.TheyaimtoreflectthespectrumofapplicationsdevelopedandsimulationsconductedattheHLRS.However,theycanonlygivethereaderaglimpseintothewealthofapplicationsthataredevelopedinover80ofourusersprojectsallyearround.

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Improvingthefuelefficiencyofaircrafthasbecomeanimportanttaskwithinthelastdecades.Notonlydoairlinesbenefitfromsavingincreasinglyexpensivefuelbutalsotheenvironmentalaspecthasgainedgrowinginterestanditwillonlybeamatteroftimeuntilenviron-mentallawslimitinggreenhousegasemissionswillbeapproved.Currentcommercialsfornewlydesignedair-craftsshowthedemandformoreeffi-cientairplanes:“The787Dreamlinerisusing20%lessfuelthananyotherair-planeofitssize”(Boeing)or“TheA380providesthelowestfuelburnperseat–whichallowsairlinestosubstantiallyreduceCO2-emissionswhileachievingprofitable,sustainablegrowthforde-cadestocome”(Airbus).

Todate,realizedoptimizationsfornewairplanesarelimitedtoenhancedshaping,avoidingtooroughsurfaces,andengineimprovement,butlittlepotentialisthoughttobeleftinthese

fieldsexceptsurfacequalityonaerodynamicsurfaces.New

conceptshavethereforetobeenvisagedthatcon-

sidertheunderlyingfluiddynamics

phenomena

indetail.Inflighttestswithakindofshark-skinsurfacestrivingforturbulentboundary-layerdragreductionhaveshownimprovementsonlyintherangeofveryfewpercents.Laminarflowcon-trol(LFC)ontheotherhandprovidesatotaldragreductionpotentialofe.g.16%byrealizing40%laminarboundary-layerflowonwingsandcontrolsur-facesofacurrentairliner[5].There-foreitisthemostpromisingcandidateforexpedientdragreduction.

Maintaininglargeregionsoflaminarboundary-layerflowhasbeenprovenfordecadesnowintwo-dimensionalsituationsbyapplyingboundary-layersuctionwhichefficientlydelayslaminar-turbulenttransition.Atypicalairlinerwing,however,issweptback(cf.figure1)toallowforhighercruisespeedatacceptablepressuredrag.Theevolv-ingcrossflowcomponentinsidetheboundarylayercausesanew,dominantinstabilitymechanism,andastraight-forwardimplementationofthetwo-dimensionalsuctionsetupsisnotpossible.Forathree-dimensionalboundarylayeritturnsoutthat-typicallysteady-longitudinalcrossflowvorticesevolveduetothenewprimaryinstabil-ityoftheflow.Whilestillbeinglaminarthesevortices-ifgrowntolargeampli-tudes-arehighlyunstabletoubiquitousunsteadybackgrounddisturbances.Duetotheextremelylargegrowthratesofthisso-calledsecondaryinstability

Laminar Flow Control in Vortex-deformed Swept-Wing Flows: Pinpoint Suction

Golden Spike Award by the HLRS Steering Committee in 2010

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Figure1:Consideredintegrationdomainwithinviscidflowstreamline.

Figure2:Vortexvisualization(snapshots)intheboundary-layerflowonanaircraftwingofareferencecase(left)andacasewithpinpointsuction(right).Toscale.Thecolorschemeshowsthewall-normalcoordinate.Suc-tionholesaremarkedbyblackcirclesatthewall.Themainflowisfrombottomlefttoupperright,andthemaincrossflowfromrighttoleft.

laminar-turbulenttransitionsetsinrapidly,typicallyafteronlyfewpercentsoftheairfoilchordlength.Thecrossflowvorticesaregeneratedbyevenminutesurfacenon-uniformity.

Boundary-layersuctiondiminishesthecrossflowbysuckinghigh-momentumfluidtothewall,andthusalsoattenu-atescrossflowinstability.However,thetypicallyapplieddiscretesuction

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Figure3:Vortexvisualization(snapshots)ofthreesetupstoillustratethepinpointsuctionconcept.Toscale.Red:refer-encecasewithoutsuction.Green:vorticesgeneratedbysuctionholeswithoutoncomingvortices.Blue:(non-linear)superposi-tion–appliedpinpointsuction.

holescan

generate,ontheother

hand,relativelylargeinitialcross-

flow-vortexdistur-bances,jeopardizing

theLFC.

Improvedsuctionconceptsforthiskindofthree-dimensional

wingflowshadthereforetobedeveloped.Messing&Kloker[4]

proposedanideacalleddistributedflowdeformation(DFD),inparticularformativesuction.Bydesigningasuit-ableslot-suctionpanelusefulvortices-withamuchcloserspanwisespacingthantheturbulence-triggeringones-arecontinuouslyexcitedandmain-tainedthatareknowntobestablewithrespecttosecondaryinstabilityandsuppressthenocentvortices.Laminar-turbulenttransitioncouldbedelayedsignificantly.

Anewideaofdirectlyinfluencinglarge-amplitudecrossflowvorticesandsecondaryinstabilitiescalledpinpointsuctioniscurrentlydeveloped[1,2,3].Thescenarioconsideredcontainstheharmful,secondarilyunstablecross-flowvorticesthatdevelopnaturally.Localized,strongsuctionthroughfewholesonlyattheupdraftsideofeachvortex,i.e.thelocallymostunstableregionwithhigh-shearlayers,directlyreducesthesecondarygrowthwhilealsoreducingthevortexstrength.

Iftheexactpositionofthevortexisknownthissuctionsetupturnsouttobeveryeffectiveandthetransi-tionlocationcanbeshiftedfardown-stream.Notethatthisisaninherentlynon-linearthree-dimensionalprocesscomparedtostandardsuctionwithitsmuchlowersuctionvelocity.

Figure2showstwosnapshotsofvorticalstructures.Ontheleftsideareferencecaseisshownwherethreesteadycrossflowvorticescanbeob-served.Apulse-likedisturbanceistrig-geredupstreamoftheshowndomainatextremelylowamplitudes,eventu-allyundergoingsecondaryinstability.Thesedisturbancesgrowrapidlyandsoonfinger-likesecondarystructurescanbedetectedthattriggerlaminar-turbulenttransition.Ontherightsidethedevelopmentofthesamevorti-ces(containingtheidenticalpulse)isshown,nowbeingsubjecttopin-pointsuction(cf.alsofigure3).Ninecloselyspacedholespervortexareplacedattheupdraftsideoftherespectivevortex(blackholesatthewall)andtransitionispreventedintheconsidereddomain.

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•TillmannA.Friederich•MarkusJ.Kloker

InstitutfürAerodynamikundGasdynamik,UniversityofStuttgart

References[1] Bonfigli,G.,Kloker,M. Secondaryinstabilityofcrossflowvortices: validationofsecondaryinstabilitytheoryby DNS,2007,JournalofFluidMechanics, 583,pp.229-272

[2] Friederich,T.,Kloker,M. DirectNumericalSimulationofSwept-Wing LaminarFlowControlusingPinpoint Suction,in:HighPerformanceComputing inScienceandEngineering'10 (eds.Nagel,W.E.,Kröner,D.B.,Resch,M.M.), TransactionsoftheHLRS2010, pp.231-250,Springer

[3] Friederich,T.,Kloker,M. NumericalSimulationofCrossflow- TransitionControlusingPinpointSuction, 2011,in:NewResultsinNumericaland ExperimentalFluidMechanicsVIII(eds.n.n.), NNFM,reviewedcontributionstothe17. STAB/DGLR-Symposium,Nov.2010, 8pages,inpress,Springer

[4] Messing,R.,Kloker,M. Investigationofsuctionforlaminarflow controlofthree-dimensionalboundary layers,2010,JournalofFluidMechanics, 658,pp.117-147

[5] Schrauf,G. Statusandperspectivesoflaminarflow, 2005,TheAeronauticalJournal(RAeS), 109,no.1102,pp.639-644

Setupswithidenticalover-allsuctionbutthroughslitsor

homogeneouslypermeablewallemployingasmallermaximumsuctionvelocityonalargerareaturntobefarlesseffective.

Allresultswereob-tainedusingspatialdirectnumericalsimula-tionswith(incompressibleandalsocompressible)in-housecodesoftheIn-stitutfürAerodynamikundGasdynamikattheUniversity

ofStuttgart.Ourlargestset-upsofupto109gridpointsrequire0.4TBRAMonNECSX-8andSX-9vectorcomputersoperatingatover1.1TFlop/s.Scenarioswithmorecomplexdomains,e.g.containingsuc-

tionchannels,willrequirelargercomputationaldomainsandhencemorepowerfulsupercomputersinthefuture.

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IntroductionToday,helicoptertechnologystillposesseveralunsolvedproblemsinaero-dynamics.Theaccuratenumericalpredictionofcertainfluid-structureinteractionalflowphenomenaofflightdynamicrelevanceisoneexampleforsuchproblemareas.PrototypingofhelicopteraircraftisincrasinglydonebasedonComputationalFluidDynamics(CFD).Helicopteraeromechanicalstud-iescallforamorewholisticapproachinthesimulationofflowfieldsinthattheyrequiretheincorporationofelasticdeformationsofflexiblestructuressuchasthemainrotorbladesintothecom-putationaltoolchain.ThisresultsinaCFD-CSD(ComputationalStructural

Dynamics)coupledsimulation.Addi-tionally,aprocedurefortrimmingtherotortowardssomeprescribedflightdynamicstatehasprovenessential.Onlyinfulfillmentofthesepresuppositionscanreasonablecomparabilityofsimula-tionandexperimentbeguaranteed.

Moststate-of-the-artsimulationen-vironmentstodayuseaaso-calledstructuredgridapproach,i.e.thefluidvolumetobesimulatedissubdividedintocuboidalelementsforwhichtheequationsofmotionforthefluidcanbesolvedinannicelyorderedmanneralongthedirectionsofthree-dimensionalspace.Recently,CFDsolutionmethodsfollowingadifferentapproachtermed

Fluid-Structure coupled Flow Simulations of Helicopter Rotors


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Figure1:Rotorwake/tailinterationsofthetailshakephenomenon

Figure2:Verticalforcesontherotordisk;left:unstructured,right:structuredsolver

unstructuredhavegainedpopularity.Here,theelementsforthediscretiza-tionofthevolumetobesimulatedcanbeofquitearbitraryshape,givingupthenicestructureofthesystemofequationsinreturnofincreasedgeo-metricalflexibility.

Asmentionedabove,quantitativenu-mericalinvestigationsofinteractionalphenomenaproblemsisoneoftheto

dateunsolvedissuesinhelicopteraero-dynamics.Anexamplewithinthisfieldistheso-calledtailshakephenomenon[1],whereinfastforwardflightconditionsinterationsofthemainrotorwakeandthetailboomandfinstructureexcitealateralbendingclosetothefrequen-ciescorrespondingtothelowerelasticmodesofthefuselagestructure.Figure1displaysthisscenario(figuretakenfrom[1]).Thisphenomenonexhibitsan

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Figure3:Vortexsystem(greyshadesandcolorfulverticalplanes)oftheflowfieldinslowforwardflight.Colorsontherotorbladesshowthepressuredis-tribution(blue:low,red:highpressure)

undesirablerandomcharacterofunsteadynaturewhichcanbefeltbytheflightcrewaslateral“kicks”[1].Todate,CFDmethodsareincapableofpredictingandexplainingthecharacteroftailshakeevenbeforeearlyprototypeflighttestingandasaconsequencemanywell-knownhelicoptertypessuchastheEurocopterEC135[2]ortheBoeingAH-64D™LongbowApache™[3]showedthiseffectinearlyflighttesting.

SimulationTheCFDsimulationofsuchphenomenarequiresaverydetailedmodellingofthestructureoftheflowfieldandthushighgeometricaldetailespeciallyinthehubareaofthemainrotorisdesired.Here,theabovementionedconventionalstruc-turedgridapproachsuffersfromthedrawbackofexcessivemanualtimecon-sumptionandeventuallybecomesimpos-sible.Therefore,anewtoolchainbasedontheunstructuredgridapproachhasbeendeveloped.Firstresultsofthenewsimulationenvironmentarecomparedtothealreadyexitingstandardstructured-gridbasedtoolchain[4].Inbothcases,afluid-structurecouplingproceduretermedtheweakcouplingapproach[5]hasbeenemployed.Here,periodicdataisexchangedbetweenCFDandCSD.Inparalleltothis,analgorithmfortrimmingtherotorensuresthatspecifiedrotorforcesandmomentsaremet.Hereby,controlinputswhichareusuallysteeredbythepilotsuchasthecollectiveandcyclicpitchanglesareadaptedinordertoeventuallymeettheseloadsalsotermedtrimobjectives.

Inapresentstudy,helicopterrotorCFD-CSDcalculationswhereperformedmakinguseofthestandardstructuredaswellasthenewunstructuredapproach.Thebasisofthecurrentinvestigationwerewindtunnelexperimentsofa

generichelicopterconfigurationwhichwereperformedintheopentestsectionoftheGerman-Dutchwindtunnel(DNW).Thesimulationcontainedafour-bladedrotorinslowforwardflightconditions.

Figure2showsthecomparisonbetweenthetworespectivetoolchainsintheverticalforcesontherotordiscplanewheretheflightdirectionisdirectedfromrighttoleft.Bothapproachesyieldaverysimilardistributionofthe

forcesontheadvancing(0..180°)aswellasontheretreatingside(180..360°).CFDmethodsalsoallowforadetailedanalysisofthefluidvolume.InFigure3avortexvisualizationofthecomplete

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•FelixBensing•ManuelKeßler

InstitutfürAerodynamikundGasdynamik,UniversityofStuttgart

flowfieldpasttherotorisdisplayed.Thevorticesshedatthetipandtheinnerrootsectionofthebladesaswellasthegreatrolled-upvorticestotheleftandrightoftheentirerotorpropagatingbackwardsareclearlyvisible.Therotorisrotatinginaclockwisesense,showingdistinctsuctionareasoflowpressureattheouterfrontpartsoftheblades(bluecolorontheblades).Twoverticalslicesthroughthevortexsystemindicatethevortexcorelocations(bluecolor).

rescalabilityofthecomputationalsetuptodifferentnumbersofcomputingunits,thenewunstructuredapproachallowsforeasyrepartitioningandcustomizationtoalmostarbitrarynumbersofcomputingprocesses.Additionally,thenewtoolchainshowedgreatpotentialinthescalabilitytoseveralhundredsofcomputingpro-cessesincomparablysmallsetups.Performanceofthecodeamountedto11GFlopswhichtranslatesintoanode-widepeakperformanceof12%makinguseoftheNECNehalem'sIntelXeonX5560processor.Thisflexibilitywillallowforthecomputationofextremelychallengingsimulationsinthefieldofhelicopteraerodynamicsbothintermsofproblemsizeaswellasgeometricalcomplexibilityintheadventoftheaccesstoevenlargercomputationalresources.

References[1] deWaard,P.G.,Trouvé,M. Tailshakevibration,1999,NLR-TP-99505, NLR,TheNetherlands

[2] Kampa,K.,Enenkl,B.,Polz,G.,Roth,G. AeromechanicAspectsintheDesignofthe EC135,1999,Proceedingsofthe23rd EuropeanRotorcraftForum,Dresden, Germany

[3] Hassan,A.A.,Thompson,T., Duque,E.P.N.,Melton,J. ResolutionofTailBuffetPhenomenonfor AH-64D™LongbowApache™,1997, Proceedingsofthe53rdAnnualForumof theAmericanHelicopterSociety,Virginia, USA

[4] Kroll,N.,Eisfeld,B.,Bleecke,H.M. FLOWer,in:NotesonNumericalFluid MechanicsViewegBraunschweig,1999, Vol.71,pp.58-68

[5] Altmikus,A.,Wagner,S.,Beaumier,P., Servera,G. AComparison:WeakversusStrong ModularCouplingforTrimmedAeroelastic RotorSimulations,AmericanHelicopter Society,58thAnnualForum,Montreal, Canada,2004

PerformanceThepresentcalculationswereperformedontheNECNehalemclusterattheHighPerformanceComputingCentre(HLRS)inStuttgart.Whilethestandardstruc-turedtoolchainposeslimitationsonthe

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Aircraftdesignhasalwaysbeenastruggleforthemost“efficient”formormethod.Themostefficienthasbeendefinedeitherbyincreasedpeakper-formanceorinmorerecenttimesbyreducedoperationalcost.Inengineeringtermsreducedoperationalcostequalsreducedorconstantdragwithconstantorincreasedlift.Today'scommercialairlinerscommonlyencountersocalledflowseparationonthewingflapsduringlandingwhichledtotheemploymentofrathercomplexandheavyflapmechan-icswhichbecamenecessarytocom-pensateforthereducedlift.Thereforethesuppressionofsuchflowseparationconstitutesavitalsteptowardsmoreefficientwingandflapdesign.Thecauseforflowseparationarepositivepres-suregradientsalongthestreamlinewhichactasobstacletotheoncom-ingfluid.Inordertoenablethefluidtoovercomelargeradversepressuregradientsithastobeenergized(seeFig-ure1).Onemethodtodosoistomovehighenergyfluidoutoftheundisturbed

freestreamtowardsthesurface.ThenecessaryvorticalmotionisinducedbyalargelongitudinaleddycreatedwithsocalledVortexGenerators(VG).Alreadyinusearepassivevortexgenerators,i.e.smallsheetsattachedtothewingsurface.IndeedtheseVGsarewellabletogenerateaforementionededdiesbuttherearealsodisadvantagesduetothefactthatthepassiveVGsareoptimizedforaspecificpointofoperationandinduceparasiticdragatallotherflightattitudes.In1992experimentalworkbyJohnstonetal.[1]hasshownthegen-eralabilitytosuppressflowseparationusingso-calledJetVortexGenerators(JVG).ThemajoradvantageoversolidVGsstemsfromthefactthatJVGsareactivelycontrollableandthusdisposeofanyadditionaldragoutsideofthepointofoperation.ThesepromisingfindingsledtointensiveresearchinJVGsandcumulatedinexhaustiveexperimentalparameterstudiescoveringmanyas-pectssuchasvelocityratio,radiusandblowingangle.

Albeittheoutcomesoftheseexperi-mentsyieldaverygoodgeneralideaofthemechanismsofactiveflowcontroldevicestherearestillanumberofopenquestionsinvolvedasnodetailedpictureoftheformationofthevortexanditsinteractionwiththeboundarylayercouldbegainedfromexperimentsyet.Therefore,anydesignsugges-tionsrelyheavilyonempiricaldataandaredifficulttotransposetodifferentconfigurations.Duringindustrialde-signprocessesparameterstudiesarethusoftenundertakennumericallybyFigure1:SchemeofJetVortexActuator

Direct Numerical Simulation of Active Separation Control Devices


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Thecomputationaldomainconsistsofarectangularbox.Boundarycondi-tionsareappliedtodefineaninflow,outflow,freestreamandwallregion.Thefundamentaldifferentialequationsareconvertedintoapproximatedif-ferenceequationsusingfinitediffer-encesonastructuredgrid.Thefinitedifferencestencilsarechosentobeofcompactform,i.e.informationofbothflowvariableanditsderivativeistakenintoaccountresultinginanumericalschemeofspectral-likewaveresolutioninspace.Theevolutionintimeissimu-latedbyanexplicitRunge-Kuttatimeintegrationschemewhichmaintainstheaccuracyofthespatialresolution.Theresultinglinearsystemofequa-tionscanbesolvedveryefficientlyonvectorCPUsupercomputersliketheNECSX-9atHLRSStuttgartbecauseofthestronglystructuredform.Fur-thermorethedomaincanbesplitintoboxesofequaldimensionswhicharethenassignedtoonecomputational

Figure2:Instantaneousstreamwisevelocitycontoursandisosurfacesofvorticityvisualizedbyl2criterion(b=150°)

meansofReynoldsAveragedNavier-Stokes(RANS)methods.ThecruxofRANSliesinthefactthattheinvolvedmodelassumptionshavetobeadaptedtoeverynewconfigurationandinthefactthattheunderlyingequationsareinherentlysteadystateandthusdonotreallyallowforsimulationoftransientprocesses.WithinthiscontextthepresentedworkcoverssimulationsofagenericJVGconfigurationbymeansofhighlyaccurateDirectNumericalSimulation(DNS)methods.TheDNSapproachisusedforitslackofanymodelassumptions.Therefore,itiswellsuitedtoprovideareferencesolutionforcoarseror“moreapproxi-mate”numericalschemes.Further-more,DNSallowsforacomputationoftheunsteadyflowformationespe-ciallyinthebeginningofthevortexgenerationanddetailedanalysisofthefluiddynamicsinvolved.

Navier-Stokes3DThenumericalsolverNavier-Stokes3D(NS3D)hasbeendevelopedattheInstitutfürAero-undGasdynamik(IAG)atStuttgartUniversity[2].TheprogramisbasedonthecompleteNavier-Stokesequation,i.e.noturbu-lenceorsmallscalemodelsareused.

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processeach.Interprocesscom-municationisrealizedbyuseofMes-sagePassingInterface(MPI)routines.WithineachMPIprocessthedomainisfurthermoreparallelizedemployingNECMicrotaskingshared-memorypar-allelization.AllcomputationsdescribedherehavebeenrunononenodeoftheNECSX-9within12hrscomputingtime.

JetVortexGeneratorSimulationsNumericalsimulationshavebeendonefortwoJet-in-Crossflowconfigurationswhichdifferinthejetexit’sorienta-tiononly.Theconfigurationswerechosentocloselymatchexperimentsdescribedin[3]andarerepresenta-tiveforseparationcontroldevices.ThecrossflowrepresentsalaminarboundarylayeronaflatplatewithzeropressuregradientatfreestreamMachnumberofMa=0.25whichcorre-spondstolandingspeedforcommer-

cialairliners.Thejetisdescribedbyasteadyvelocitydistributionatthewallboundaryofthecomputationaldomainandresemblestheprofileofapipeflow.Thejet-to-crossflowvelocityratioissettoR=3.Thejetsareinclinedbyanangleofattacka=30°andskewedtothedownstreamdirectionbyanglesb=30°andb=150°respectively.Onecanimagineanobliquejettobeblowneitheragainsttheoncomingmainfloworinlinewithit.Altogether18.4mgridnodesareusedandcomputationshavebeencarriedoutfor31flowthroughtimes.Albeitthisdoesnotsufficetoprovidedataforstatisticalanalysis,ityieldsagoodpictureoftheevolutionoftheperturbedflowfield.Eventhoughboththejetandthelaminarboundarylayerareinitiallyinasteadystatetheresultingflowregimebecomeshighlyunsteadywiththejetexhibitingun-stablemodesleadingtotheformationoflargetransientvortexstructures

Figure3:Instantaneousdownstreamvelocitylocityandstreamlines(b=30°)

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reachingfaroutoftheboundarylayer(Figure2).Thejetaffectsthebound-arylayerbymainlytwomechanismsnamelytheblockageoftheoncomingfluidandthestrongshearatthejetslopes.Theblockageleadstoflowstructuressimilartothewakesfoundbehindsolidobstacles,i.e.periodiceddysheddingclosetothewallandasocalledhorseshoevortexwhichwrapsaroundthejet.Furthermoretheoncomingflowdeflectsthejetintothemainstreamwisedirectionwhichleadstoadditionalflowfeaturesuniquetojetsincrossflowsuchastheentrain-mentofnearwallfluidlayersupwardsintothejetwake.Boththeinducedperturbationsofthecrossflowbehindthejetexitaswellasthefluidentrain-mentevidentlydependonthejetpa-rameters.Theshearontheslopeofthejetontheotherhandinducesaro-tationalmotionwhichisprolongedandfortifiedbehindthejetexitandleads

totheformationofalargelongitudinaleddyabovethewall.Inordertogainaninsightinthefluiddynamicssnapshotsoftheflowfieldarerecordedandevaluated.Figures3and4depicttheflowatthelastrecordedtimestep.Incaseofaskewangleofb=30°twolon-gitudinalvorticesestablishintheflowandwraparoundeachotherduetotheinducedvelocitiesinthetransver-salplane.Thedevelopmentofahigh-speedstreakclosetothewalltakesplacewithaninclinationtothecenter-lineoftheflow.Thetwolongitudinalvorticesseemnotsubjecttoinstabili-tiesatthatpointintime.Theystretchratherwell-defineddownstreamandupwardsalongtheplate.Theoncom-ingfreestreamdeflectsthejetinbothspanwiseandwall-normaldirection.Thusastrongshearlayerdevelopsonthetopsideofthejet.Thesweepofthejetovertheboundarylayerresultsinthenear-wallfluidlayersbeingen-

Figure4:Instantaneousdownstreamvelocityandstreamlines(b=150°)

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trainedupwardsintothetrajectoryofthejet.Theentrainmentregiondoesnotextentveryfardownstreamthoughandtheexchangeofhigh-andlow-speedlayersinsidetheboundarylayerisnotverystrong.Adifferentflowcanbeobservedwhenthejetisblownagainstthemainflow.Firstlytheblock-ageisalotstrongerthanintheprevi-ouscase.Secondlythewakeexhibitsinstabilitieswhichleadtoamixingofthefluidlayersinalldirections.Againthejetwakegetsdeflectedbutaddi-tionallytheentrainmentregionextentsfurtherdownstreamandthewakerollsintoalongitudinalvortex.

BoundaryLayerControlAsalreadymentioned,Jet-in-Cross-flowconfigurationsareinvestigatedasameanstosuppressboundarylayerseparation.Thisistobeachievedbymovingfasterfluidlayersclosertothewallandtherewithincreasingthewallfriction.Figuredepictsacompari-sonofthetime-averagedwall-shear-stressdistributionforthesimulatedcases.Bothconfigurationsleadtoanetincreaseofwallshearstressinaconfinedstripebehindthejet.Thisareaoverlapsthetrajectoryofthejetonlymarginallythoughincaseofajetangleofb=30°.Theadditional

Figure5:Comparisonofmeanwallsheardistributioninactuatedflow(top:b=150°,bottom:b=30)

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• BjörnSelent• UlrichRist

Instituteof Aero-and Gasdynamics, University ofStuttgart

energyisfedtotheboundarylayerinanoverlystraightforwardfashionbyaddingmomentum.Inordertoexploitsuchamechanismwithreasonableinput-to-outputratioatangentialjetseemsmoreadvisable.Ontheotherhandwhenthejetisdirectedagainstthemainflowtheregionofincreasedshearextendspromisinglyinbothdownstreamandspanwisedirection.Thereasonbeingthedifferentfluiddy-namicsatwork.Firstlythejetactsasturbulatorwhichleadstofullervelocityprofilescomparedtotheunperturbedflowandfurthermorethejet-crossflowinteractionleadstotheformationofalongitudinalvortexwhichtransportsfastfluidlayersclosertothewall.Thereforethisconfigurationseemsfeasibleforactiveflowcontroldeviceseveninalreadyfullyturbulentshearflowsastheyarefoundonaircraftairfoilsandflaps.

ConclusionSimulationsoftwodifferentJet-in-CrossflowconfigurationshavebeencarriedoutinordertoinvestigatetheeffectonaboundarylayeratMa=0.25.Thejetshavebeenskewedandinclinedbyvaluestypicallyfoundinactive-flow-controldevicesetups.Thesimulationsgaveinsightintothegenerationofajet-vortexsystemandwakeperturbations.Incaseofajetalignedwiththemeanflowastableflowestablishedinwhichtheboundarylayerwasonlypoorlyenergizedbythejet.Askewofthejetagainstthemainflowledtothegenera-tionofalongitudinalvortexwhichinturnledtosignificantlyincreasedwallfriction.Followingsimulationsmayin-cludefurthervariationofthepitchandskewanglesofthejetaswellasveloc-ityratiovariation.Alsoofinterestisachangeofinitialconditionstowardsafullydevelopedturbulentboundarylayer.

AcknowledgementsWethanktheHighPerfomanceComputingCenterStuttgart(HLRS)forprovisionofsupercomputingtimeandtechnicalsupportwithintheproject“LAMTUR”.

References[1] Johnston,J.P.,Nishi,M.

“VortexGeneratorJets–aMeansforFlowSeparationControl”,AIAAJournal,28(6),pp.989–994,1990

[2] Babucke,A.,Kloker,M.,Rist,U.“DirectNumericalSimulationofaSerratedNozzleEndforJet-noiseReduction”,inM.Resch(Eds.),HighPerformanceComput-ing,inScienceandEngineering2007,Springer,2007

[3] Casper,M.,Kähler,C.J.,Radespiel,R.“FundamentalsofBoundaryLayeraControlwithVortexGeneratorJetArrays”,in4thFlowControlConference,numberAIAA,pp.2008-3995,2008

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Figure1:Hybridblock-unstructuredmeshforthespherecalculation

Thisarticledescribestwocalculationsperformedforthenumericalsimulationofascram-jetintakeatahighMachnumberofM=8ontheHLRBIIatLRZ.ThisprojectispartoftheDFGGradui-ertenkollegGRK1095/2,-conceptionofascram-jetdemonstrator-anditspartnersatTUMünchen,RWTHAachen,UniversitätStuttgartandtheDLR.Inordertopavethewayforthesimulationoftheverycomplexintakeflowfield,preparatorycalculationswereperformedtotestcertaincodefeaturesandtoprovideinsightintolargeparallelcomputations.Thesecalculationsaredescribedinthisarticle.

NumericalCodeSinceallpresentednumericalproblemscontainstronglytimedependentin-stationaryphenomena,weareusingaspecialunstructuredexplicitdiscontinu-ousGalerkincode,calledHALO(HighlyAdaptiveLocalOperator).Thiscodeisofhighorderofaccuracyinbothspaceandtimeandcanhandleunstructuredhybridgridswithhangingnodes,consistingoftetrahedra,hexahedra,prismsandpyramidsin3Dandevenpolygonsin2D.Itisalmostfullyparal-lelizedandrequiresonlyaminimumofinter-processorcommunicationduetoitsexplicitcharacter.Amainfeatureofthiscodeisitstimediscretizationmethod[1,2,3],thatallowsahighorder

High Order large Scale Calculations

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Figure2:Instantaneousl2isosurfaceofthesphere

timeconsistentlocaltimesteppingmechanism,whereineachgrid-cellcanadvancewithitsownmaximumpossibletimestep.Variousequationsystemsareimplemented,suchasEulerorNavier-StokesequationsaswellasviscousMagnetohydrodynamic(MHD)equations.AllpresentedcalculationsemploytheNavier-Stokesequations.Tosuppressoscillationsatdiscontinuitiessuchasshocks,artificialviscosityisaddedtosmeartheshockprofileresultinginstablecomputations.

Scale-upEfficiencyTotestthescalingcapabilitiesoftheHALOcode,wesetupanexampleforwhichaperfectloadbalancewasachievable.Wewereusingtheso-calledmanufacturedsolutiontechniqueforthe3DcompressibleunsteadyNavier-Stokesequations:InsertingananalyticalfunctionintotheNavier-Stokesequa-tionsleadstoarighthandsidethatisprescribedasasourceterminthenumericalcode.

Theproblemwassetupwithperiodicboundariessothattheboundarycom-municationwillnotdifferfromtheinter-processorcommunications.Thesizeofthecomputationalproblemwasincreasedinparallelwiththenumberofprocessorsforcalculation.Thisway,wekeptaconstantloadincomputationaswellasincommunication.

Table1showsthegoodscale-upeffi-ciencyoftheHALOcodeforupto4,080processorswithaconstantloadperprocessor.

Nb.ofprocs 1 1,000 2,197 4,080

Efficiency(%) - 99.1 97.8 98.8

Table1:Scale-upefficiencyoftheHALOcode

TheefficiencywhencalculatingonNprocessorsiscalculatedasthecalculationtimeononeprocessordividedbythetimeneededforacalculationonNprocessors.

PerformedCalculationsInordertobeabletoperformalargecalculationofthescram-jetwithfea-tureslikehp-adaption,shockcapturingandVMS,preparatorycalculationsweresetuptotestcertainfeaturesandpre-parethecodeforefficientcomputationonalargenumberofprocessors.Thesecalculationstacklephysicalproblemsandwillbedescribednext.

3DFlowaroundaSphereThelaminartimeperiodicflowaroundaspherewassetuptotestthecodesabilitytohandleunstructuredhybridgridsandap-adaptionmechanisminthreespacedimensions.WeweresolvingtheunsteadycompressibleNavier-Stokesequationswithafree-streamMachnumberofMa=0.3andaReynoldsnumberofRe=300.Theproblemwasdiscretizedwithablock-

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Figure3:Distributionofthelocalpolynomialdegreeatendtimeofthespherecalculation

unstructuredgridconsistingofprismsfortheboundarylayer,tetrahedraandhexahedraelsewhere.Figure1showsthedifferentgridblocksanddimensionsofthecomputationaldomain.P-adaptionwasarrangedsothateachgridcellwasallowedtoadaptitspolynomialdegreebetween1and5every500timesteps.

Todemonstratethecalculationresults,Figure2showsa3Dviewoftheinstan-taneousvortexmeasureλ2forthistestcase.Thecolorlevelsindicatetheveloc-itymagnitude.Here,onecaneasilyseethattheverylargecellsattheendofthewakecannotprovidethenecessaryresolutionandarethereforeproducinglargeintercelljumpsofthesolution.

Finally,Figure3showsthedistributionofthelocalpolynomialdegreepatendtimetend=1,000.

3DFreestreamInjectorThiscalculationtargetsshockcapturingandp-adaptioncapabilitiesofthescheme,aswesimulateaMa=1.4,Re=30,000injection.

Theinjectionnozzleisdesignedaccord-ingtospecificationsforgasinjectiondevicesusedintheautomotiveindustry.Thisproblemalsocontainscomplexcurvedgeometriesthatarechallengingforhighorderschemes.Theobjectiveofthiscalculationistheaeroacousticsimulationoftheinstationaryinjectionprocess,includingthestartupoftheprocess.Resultscanbevalidatedwithcalculationsperformedwithothercodes,bothatourinstituteaswellasinindustry.Preliminary2Dcalcula-tionsalreadyprovidedinsightintothenecessarygridresolutionsandshockcapturingstrategies.Inourcase,theproblemwascalculatedonagridwithover16millionDGdegreesoffreedom

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Figure4:Densitydistributionandvelocitystreamlinesofthe3Dfreestreaminjector

•ChristophAltmann•GregorGassner•MarcStaudenmaier•Claus-DieterMunz

InstituteofAero-andGasdynamics,UniversityofStuttgart

(4millionofhexahedralelements)on500to1,000processors.Figure4showsa2Dsliceplaneofthecalculation(densitydistributionandvelocitystream-lines)togetherwithanisosurfaceplotofthedensitythathighlightsthedevelop-mentoftheflow.

Toillustratetheflowdevelopment,severalstreamlineshavebeenadded.Pleasenotethatthepicturepresentsanearlyphaseofsimulation.Thegeom-etryitselfisrathercomplex,consistingoffourkidney-shapedinjection“nozzles”withinthecylindricalinjectorandisas-sembledwithunstructuredhexahedra,allowinghangingnodesandpolygonsatconnectionsurfaces.

CodePerformanceTheHLRBIIprovidesaneasyjobperformancesummarydirectlyinthecommandline.Thistoolcanprovideafirstinsightintotheperformanceofthecalculationsandhelptodeterminethecomputationalefficiencyofthenumeri-calcode.Weherebydiscoveredstrongperformancedifferences,dependingonthetestcase.Especiallythescale-uptestsaswellasthespherecalcula-tionwhereabletoperformwithupto700MFlop/sperprocessor.Thelatterperformancewasdiscoveredforthe4,080processorscale-upcalculationresultinginatotalofabout3TFlop/s.Itwasfoundthatcertainsettingsofp-adaptivityandmeshstructuresdohaveasignificantinfluenceonthecodeperformance.

OutlookThepresentedcalculationsdidnotonlyshowthepotentialoftheHALOcodebutalsogaveaninsightintothephysicstheywereaddressingandhelpedtoimprovethecode.Thecodeshouldnowbereadytotargetthefullintakesimulation.

References[1] Lörcher,F.,Gassner,G.andMunz,C.-D. AdiscontinuousGalerkinschemebased onaspace-timeexpansion.I.Inviscid compressibleflowinonespacedimension. In:JournalofScientificComputing,Vol.23, No.2,pp.175-199,2007 DOI=http://dx.doi.org/10.1007/s10915- 007-9128-x

[2] Lörcher,F.,Gassner,G.andMunz,C.-D. AdiscontinuousGalerkinschemebasedon aspace-timeexpansion.II.Viscousflow equationsinmultidimensions.In:Journal ofScientificComputing,Vol.34, No.3,pp.260-268,2008 DOI=http://dx.doi.org/10.1007/s10915- 007-9169-1

[3] Lörcher,F.,Gassner,G.andMunz,C.-D. AContributiontotheConstructionof DiffusionFluxesforFiniteVolumeand DiscontinuousGalerkinSchemes.In: JournalofComputationalPhysics,Vol. 224,No.2,pp.1049-1063,2007 DOI=http://dx.doi.org/10.1016/ j.jcp.2006.11.004

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Figure1:Thehoneycomblatticewithitsunitcell,displayedbythedashedredline.Theunitcellcontainstwoatoms,eachonebelongingtoadifferentsublat-tice.Therefore,thehoneycomblatticehasabipartitestructure,wheretheatomsofagivensublatticearesurroundedbysitesoftheotherone.

Intherecentpastcondensedmattersystemsdisplayedanumberofexoticstateslikeunconventionalsupercon-ductivityinhigh-temperaturesupercon-ductors,supersolidityin4He,andspinliquidsinfrustratedmangnets,thatemergeduetocorrelationsinmany-bodysystems[1].Ofparticularinterestarespinliquids,wherequantumfluctu-ationsprecludeorderingfromtheliquidstatetoasolid,i.e.toaperiodicorder-ingofthemagneticmoments,evenatzeroabsolutetemperature.Generallyitisexpected,thatsuchstrongquantumfluctuationsariseduetocompetingin-teractionsthatfrustratetheformationofanorderedstate[1].

Asystemwherequantumfluctuationsmayplayadominantroleisgraphenethatwasrecentlyobtainedexperimen-tallybyexfoliationofgraphite[2].Bymeansofsuchamicromechanicalcleavage,singlelayersofcarbonatomswithahoneycombstructure,

schematicallydisplayedinFigure1,areproduced,that,remarkably,subsistasfree-standingtwo-dimensionalcrystals.Thehoneycomblatticeisabipartiteone,i.e.itconsistsoftwosublattices,wherethenearestneighboursofthesitesofoneofthemaresitesbelongingtotheothersublattice.Hence,insuchalatticegeometricfrustrationisabsent,sincee.g.antiferromagneticorderispossiblebyplacingmagneticmomentspointinginonedirectionononesublat-ticeandpointingintheoppositeoneontheothersublattice.However,duetothefactthatthehoneycomblatticehasthesmallestcoordinationnumberintwodimensions,theeffectofquan-tumfluctuationsisthestrongest.

Afurtherremarkablefeatureofthehoneycomblatticeappears,whenelec-tronsareplacedonit.Assumingforthemomentnon-interactingelectrons,thecorrespondingbandstructureisasshowninFigure2.There,itcanbeseeninthefirstplace,thattheelectron(particle)statesandthehole(antiparticle)statesaresymmetricallyplacedaroundthezeroofenergy,suchthatparticle-hole(chargeconjugation)symmetryispresent.ThezeroofenergycorrespondstotheFermienergywhentheaveragedensityofelectronsisunityperlatticesite,i.e.forahalf-filledband.Moreover,thelowenergystatesaroundparticularpointsinthetwo-dimensionalBrillouinzonedisplayarelativisticdis-persionandcanbereadilydescribedbyDirac’sequation[2].Wetherefore,willrefertothosepointsasDiracpointsinthefollowing.Therelativisticdispersioninthelowenergysectorathalf-fillingleadstoavanishingdensityofstates

Exotic State in Correlated Relativistic Electrons

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Figure2:Energydispersionoftheelectronicstatesfornon-interactingelectronsonahoneycomblattice.Closetothezeroofenergythedispersioncorrespondstotheonecharacteristicofrelativisticfermions,asdisplayedbytheportionzoomedin.

attheFermienergy.Therefore,afinitestrengthforinteractionspromotingaspontaneoussymmetrybreakingisnecessary,enhancingthus,theroleoffluctuations.

Inordertostudytheeffectsofcorrela-tionsinelectronsonthehoneycomblat-ticeinitsmostbasicform,weconsidertheHubbardmodel,whereonlyanon-siteinteraction,termedU,ispresent.Suchamodelisaparadigmforstronglycorrelatedelectrons,asinthecaseofhightemperaturesuperconductors[3],andgivesanaccuratedescriptionofultra-coldfermionicatomsinopticallattices[4,5].ForlargevaluesoftherepulsiveinteractionU,andathalf-filling,theground-statecorrespondstoaMott-insulator,i.e.aninsulatingstateduetointeractionsincontrasttothemetallicstateofthenoninteractingsystem.Furthermore,inthislimitan-tiferromagneticcorrelationsdominateduetoPauli’sexclusionprincipleandthenecessityofgainingkineticenergy.Therefore,onabipartitelattice,theywillleadtoanantiferromagneticallyorderedground-state.However,asthe

interactionstrengthdiminishes,acompe-titionbetweenthetendencytoorderandquantumfluctuationswillsetin,sothatadetailedanalysisofcorrelationsisneededtocharacterizethepossiblephases.

Needlesstosay,anunbiasedstudyasdelineatedaboveisonlypossiblebynumericalmeans.Amongthedifferentmethods,QuantumMonteCarlo(QMC)simulationsarethemostappropriateonesinceacarefulextrapolationtothethermodynamiclimit,inthiscaseintwodimensions,ismandatorytodeterminewhetheraspontaneoussymmetrybreak-inghastakenplace.Weimplementedaprojective(temperature T=0)determi-nantalQMCalgorithminthecanonicalensemblethatisfreeofthesign-problemathalf-filling[6].Thisalgorithmallowsthecalculationoftheexpectationvalueofanyphysicalobservableintheground-statebyperforminganimaginarytimeevolutionofatrialwavefunctionthatisrequiredtobenonorthogonaltotheground-state.ThevalueΘreachedintheimaginarytimeevolutioncorre-spondstoaprojectionparameter[6].Foraspin-singlettrialwavefunction,

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Figure3:PhasediagramfortheHubbardmodelonthehoneycomblatticeathalf-filling.Thesemimetal(SM)andtheantiferro-magneticMottinsulator(AFMI)areseparatedbyagappedspinliquid(SL)phaseinanintermediatecouplingregime.Δsp(K)denotesthesingle-particlegapatoneoftheDiracpoints(K),andΔsthespingap.msdenotesthestaggeredmagnetizationwhosesaturationvalueis1/2.

wefoundΘ=40/ttobesufficienttoobtainconvergedground-statequanti-tieswithinstatisticaluncertainty.Inthepresentedsimulations,weusedafiniteimaginarytimestepΔτ=0.05/t.Weverifiedbyextrapolating Δτ→0thatthisfiniteimaginarytimestepproducesnoartefacts.Thephasesdescribedinthefollowingweredeterminedbyafinite-sizeextrapolationtothethermo-dynamiclimitwithlatticesofN=2L2siteswithperiodicboundaryconditions,andlinearsizesLintermsoftheunitcellcontainingtwosites,withL≤18.Lwastakenasamultipleof3inordertobeabletoincludetheDiracpointsinourBrillouinzones,suchthatthelowenergyphysicsiscorrectlyrepre-sented.IntermsofasimulationofaclassicalIsingmodel,inourcasewithlong-rangeinteractions,thelargestsys-temssizescorrespondtoalatticewith518,400sites.

Afirstinsightinthepossiblephasesofthesystemisobtainedbyconsideringthesingle-particleexcitationgapΔsp(k)thatweextractedfromtheimaginary-timedisplacedGreenfunction(seeRef.[7]fordetails).Δsp(k)givesthemini-malenergynecessarytoextractonefermionfromthesystem,andcorre-spondstothegapthatcanbeobserved

inphotoemissionexperiments.AsshowninFigure3,Δsp(K)=0forU<Uc≈3.6t,wheretisthehoppingamplitudeintheHubbardmodel.Thevanishinggapcorrespondstoametal,thatiscommonlycalledasemimetal(SM)duetothefactthattheFermisurfaceisinthiscasereducedtoapoint.BeyondUc,thesystementersintoaninsulatingphaseduetointerac-tions,andhence,asexpectedforlargevaluesofU,thesystembecomesaMott-insulator.ThevaluesofthegapareobtainedviaanextrapolationoftheQMCdatatothethermodynamiclimitwithenergiesgiveninunitsoft[7].

Asexplainedabove,forvalueslarge

enoughofU,oneexpectslong-range

antiferromagentic(AF)correlations.We

thereforemeasuredtheAFspinstruc-

ture-factorSAFthatrevealslong-rangeAF

orderiflimN→∞ SAF/N>0.Theresultsof

afinite-sizeextrapolationarealsopre-

sentedinthephasediagramofFigure

3.AForderappearsbeyondU/t≈4.3.

Hence,contrarytotheusualexpectation

forabipartitelattice,AFlong-range

ordersetsinlaterthantheinsulating

phase,leavinganextendedwindow

3.6U/t4.3,withinwhichthe

systemisneitherasemimetal,noran

AFMott-insulator.

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Figure4:SpingapinunitsoftasafunctionofU/tforvarioussys-temssizes.Thelowestcurvecorrespondstotheextrapolationtothether-modynamiclimit(TDL).

Furtherdetailsonthenatureofthisintermediateregionareobtainedbyexaminingthespinexcitationgap,ex-tractedfromthelong-timebehaviouroftheimaginary-timedisplacedspin-spincorrelationfunction[7].WeconsiderfirstthespingapΔsinthestaggeredsectoratk=0,whichvanishesinsidetheAFphaseduetotheemergenceoftwoGoldstonemodes,aswellasinthegaplessmetallicphase.Figure4showsfinitesizeestimatesofΔsfordifferentvaluesofU/t,alongwithanextrapolationtothethermodynamiclimit.AfinitevalueofΔspersistswithinanintermediateparameterregime3.5U/t4.3,whileitvanishesbothwithinthemetallicandtheAFphase.WealsocalculatedtheuniformspingapΔubyextrapolatingthespingapobservedatthesmallestfinitek-vectoroneachclustertothethermodynamiclimit.ΔuisfoundtobeevenlargerthanΔsinsidetheintermediateregion(e.g.Δu=0.101(8)atU/t=4),andvan-ishesinthemetallicandtheAFphase[7].

TheobservationofafinitespingaprulesoutgaplessphasessuchastripletsuperconductivityaswellasquantumspinHallstates.Theremain-ingpossibilitiescanbeenumeratedbyconsideringthecouplingtoorderpa-rametersthatleadtotheopeningofamassgapinDiracfermions,andhencetoaccountforthesingle-particlegapobservedintheQMCdata:(i)singletsuperconductivity,(ii)aquantumHallstate(QHS),(iii)chargedensitywave(CDW)order,and(iv)avalencebondcrystal(VBC).OurQMCresultsexcludeallthosestates,asdiscussedbelow.Thereby,theintermediatephaseisgen-uinelyanexoticstateofmattersinceitcannotbeunderstoodatthesingleparticlelevelwithinamean-fieldtheorywithalocalorder-parameter.

Furthermore,sincenospontaneoussymmetry-breakingisobserved,whileaspingapispresent,itcorrespondstoaspinliquidstate.

Inordertoassess,ifsuperconductivityarisesinthevicinityoftheMott-transition,weusedthemethodoffluxquantizationwhichprobesthesuper-fluiddensityandishenceindependentofthespecificsymmetryofthepairwavefunction[7].LetΦbeamagneticfluxtraversingthecentreofatorusonwhichtheelectronicsystemliesandE0(Φ/Φ0)thetotalgroundstateenergy, Φ0beingthefluxquantum.AsuperconductingstateofCooperpairsispresentifinthethermody-namiclimit,themacroscopicenergydifferenceE0(Φ/Φ0)−E0(Φ/Φ0 =1/2)isafunctionwithperiod1/2.Incontrast,ametallic(insulating)phaseischarac-terizedbyan(exponential)vanishingofE0(Φ/Φ0)−E0(Φ/Φ0=1/2)asafunctionofsystemsize.TheQMCdataisconsis-tentwiththevanishingofthisquantityinthethermodynamiclimit.Inaddition,wemeasuredsingletsuperconductingorderparametersof(extended)s-,p-,andf-wavesymmetry,whichturnouttoallvanishinthethermodynamiclimit[7].Hence,bothfluxquantizationaswell

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Figure5:Realspaceplotofthespindimer-dimercorrelations.Rightside:thedimer-dimercorrelationfunc-tioninthespin-channelfora L =6systematU/t=4.Leftside:thesamecorrelationfortheisolatedHubbardhexagonalsoatU/t=4.Thereferencebondsaredressedwithstripes.Numbersinparenthesisindicatethestandarderrorofthelastdigit.

asadirectmeasurementofpairingcorrelationsinvarioussymmetrysectorsleadtonosignofsuperconductivity.

BoththeCDWandQHStriggerabreakingofthesub-latticesymmetryandtherebyopenamassgapatthemeanfieldlevel.Adetailedanalysisofthecharge-chargecorrelationfunctionsrulesoutaCDW.Furthermore,wehavefoundnosignatureforthepresenceof(spin)currentsintheground-state.ThisrulesoutthebreakingofsublatticeandtimereversalsymmetriesasrequiredfortheQHS[7].

ToexaminetheoccurrenceofaVBC,weprobefordimer-dimercorrelationsbetweenadimerformedbynearestneighboursites<ij>andadistantbondformedbysites<kl>[7].WehavefoundnoVBC,neitherinthecharge,norinthespinsector.TheleftsideofFigure4showstheresultsofthismeasurementinthespinsector,i.e.thecorrelationbetweensingletdimersat U/t=4.0.Thestripedbondistheonewithrespecttowhichcorrelationsweredetermined.Theyarefoundtobeshort-ranged,andconsistentwiththedominanceofaresonatingvalencebond(RVB)statewithinthehexagonsofthehoneycomblattice.Thiscanbeseenby

comparingthesinglet-correlationswiththoseofanisolatedhexagon(rightsideofFigure4),theclassicalexampleoftheresonancephenomenoninconju-gated-electrons[8].Accordingly,wefindnolong-rangedorderfromthedimer-dimerstructurefactorsinFourierspace.Ourresultsthusrevealagenuinelyexoticstateofmatter,wherenospontaneoussymmetry-breakingisobserved,whileaspingapispresent.ItcorrespondstoaspinliquidRVBstateintheintermediatecouplingregimeinthevicinityoftheMott-transition.

ThepresenceofaspinliquidintheHubbardmodelonthehoneycomblatticeclosetoanantiferromagneticMott-insulatorwashithertounex-pected,duetothebipartitenatureofthehoneycomblattice,andhence,theabsenceoffrustration.However,ourresultsindicatethatstrongenoughfluctuations,thatdevelopclosetothequantumcriticalpointwhereAFordersetsin,leadtosuchanexoticstateofmatter.Itcouldbeexpected,thatsuchfluctuationswouldpromotesomebrokensymmetrystateslikesuper-conductivity.However,thevanishingdensityofstatesattheFermienergymayberesponsibleforitsabsence,

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•ZiYangMeng1

•ThomasC.Lang2

•StefanWessel1

•FakherF.Assaad2

•Alejandro Muramatsu1

1Institutfür Theoretische PhysikIII, Universität Stuttgart, Germany

2Institutfür Theoretische Physikund Astrophysik, Universität Würzburg, Germany

sinceinthiscase,afinitecouplingstrengthisneeded,atleastintheBCS-frame.

Havinganunexpectedrealizationofashort-rangeRVBstate,itwouldbehighlyinterestingtoexploretheconsequencesofdoping,inaspiritratherclosetotheoriginalscenarioproposedbyAnderson[3]andKivelsonet al.[9]forthecuprates.Inparticular,forthefullygappedshort-rangeRVBstate,thefinitespingapsetstheenergyscaleofpairinginthesuperconductingstate[9].Inthisrespect,thevalueobtainedforthespingapisratherpromizing.ThelargestvalueattainedisΔs∼0.025t(Fig.1),thatfortintherangeof1.5to2.5eV(ingrapheneist=2.8eV[2])correspondstoatemperaturescalerangingfrom400to700K.

AlthoughstudiesofdopingarebeyondthepowerofourquantumMonteCarloapproachduetothesignprob-lem,theycouldopeninterestingper-spectivese.g.infutureexperimentswithultra-coldatomsonahoneycombopticallattice,orwithhoneycomblatticesbasedongroupIVelementslikeexpandedgraphene(toenhancetheratioU/t)orSi,wherethenear-estneighbourdistanceisexpectedtobeapproximately50%largerthaningraphene[10],suchthatcorrelationseffectsareenhanced.

AcknowledgmentsWethankL.Balents,S.Capponi,A.H.CastroNeto,A.Georges,M.Hermele,A.L̈ auchli,E.Molinari,Y.Motome,S.Sachdev,K.P.SchmidtandS.Sorellafordiscussions.WearegratefultoS.A.Kivelsonforthoroughlyreadingourmanuscriptandprovidingimportantsuggestions.F.F.A.isgrateful

totheKITPSantaBarbaraforhospital-ityandacknowledgessupportbytheDFGthroughAS120/4andFG1162.A.M.thankstheAspenCenterforPhysicsforhospitalityandacknowledgespartialsupportbytheDFGthroughSFB/TRR21.S.W.acknowledgessup-portbytheDFGthroughSFB/TRR21andWE3649.WethankNICJülich,HLRSStuttgart,theBWGrid,andtheLRZMünchenfortheallocationofCPUtime.

References[1] NatureInsight ExoticMatter,Nature464,175, 2010

[2] Neto,A.H.C.,Guinea,F.,Peres, N.M.R.,Novoselov,K.S.andGeim,A.K. Rev.Mod.Phys.81,109,2009

[3] Anderson,P.W. Science235,1196,1987

[4] Jördens,R.,Strohmaier,N.,Günter,K., Moritz,H.andEsslinger,T. Nature455,204,2008

[5] Schneider,U.,Hackermüller,L.,Will,S., Best,T.,Bloch,I.,Costi,T.A.,Helmes,R.W., Rasch,D.andRosch,A. Science322,1520,2008

[6] Assaad,F.F.andEvertz,H.G. ComputationalMany-ParticlePhysics, LectureNotesinPhysics,p.739, Springer-Verlag,Berlin,2008

[7] Meng,Z.-Y.,Lang,T.,Wessel,S., Assaad,F.F.andMuramatsu,A. Nature464,847,2010

[8] Pauling,L.C. TheNatureoftheChemicalBondandthe StructureofMoleculesandCrystals:an IntroductiontoModernStructural Chemistry,CornellUniversityPress, 20thEdition,Ithaca,NewYork,USA, 1986

[9] Kivelson,S.A.,Rokhsar,D.S. andSethna,J.P. Phys.Rev.B35,8865,1987

[10]Cahangirov,S.,Topsakal,M.,Aktürk,E., Sahin,H.andCiraci,S. Phys.Rev.Lett.102,236804,2009

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Figure1:Surfacecirculation(snapshot)aroundSouthAfrica.TheAgulhasCur-rent(redband)flowsalongtheeastcoastofSouthAfrica,retroflectingbackintotheIndianOcean.DuringthisprocessAgulhasringsarecutoffanddriftintotheAtlanticOcean.

TheoceancurrentsaroundSouthAfricaareanimportantelementintheglobaloceancirculation.UnderpresentclimateconditionstheflowofwarmandsaltywatersfromtheIndianOceanintotheAtlanticOceanaroundthesoutherntipofAfrica,the“Agulhasleakage”,pro-videsthebulkoftheupperlimbofthethermohalinecirculationintheAtlanticOcean.PartsofthiswaterlaterfeedintotheGulfStreamsystemoftheNorthAtlanticthatisresponsibleforthemildclimaticconditionsinEurope.TheunderstandingofthedynamicalfactorsdeterminingtheintensityandvariabilityofAgulhasleakageisstillincomplete,andsoisitsbehaviourunderachangingclimate.

Incontrasttoitslarge-scaleimportancethecirculationintheAgulhasregionisadynamicalmixtureofdifferenttimeandspacescales(Fig.1):Astrongwesternboundarycurrent,theAgulhasCurrent,transportsthewarmandsaltywatersouthwardintheIndianOcean.SouthofAfricaitovershootsthecontinentalslopeandabruptlyturnsbackintotheIndianOcean,whilesheddingenormousmesoscaleringsofseveral100kilo-metresindiameterandextendingoverlargepartsofthewatercolumn.Theseringstransporttheheatandsaltaspul-satingelementsintotheAtlanticOcean.Thecirculationdynamicsintheregionalsoincludessmall-scaleupstreamperturbationsasanimportantelement.EddiesareformedintheMozambiqueChannelandeastofMadagascar;

thesedriftsouthwardstowardstheAgulhasCurrent,displacing

itupto200kmoffshore.Thecorrespondingmeanders

rapidlyprogressdown-streamandtriggerthesheddingofAgulhasringsandthereforeAgulhasleakage.

ToexaminetheroleofAgulhasleakageintheglobaloceaniccirculation,aninnova-

tiveoceanmodellingprogramhasbeenset

upthatadvancesnewmethodologiesdeveloped

ininternationalcooperationwithFrenchandSouthAfrican

colleagues,aspartoftheEuropeanmodelcollaborationDRAKKAR[1].

The Agulhas System as a Key Region of the global oceanic Circulation


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Figure2:SchematicsofAGRIFnesting.Time-step-pingofthebase(left)andnested(right)grids.Thegreenboxesandarrowsin-dicateaninterpolationfromthebasegridontotheouterboundariesofthenest,theredonesanaveragingoftheouterandsurfaceboundariesofthenestontothebasegrid;themeshindicatesanaveragingofthewholenestontoitsbasegridpointsintheAgulhasregion.Greyar-rowsandnumbersindicatethetimestepsofbase(Bn)andnest(Nn)andtheirre-spectiveupdates(Bn’,Nn’).

Themodelhierarchyisbasedonthe“NucleusforEuropeanModellingoftheOcean”(NEMO,v.2.3)[2],consistingofacoupledocean/sea-icemodel.Theoceancomponentisafinite-differencediscreti-zationofavariantoftheNavier-StokesEquations(the“primitiveequations”),steppingthree-dimensionalvelocities,temperaturesandsalinitiesforwardintime.Afreesurfaceformulation(e.g.byaconjugategradientsolver),ahigh-order

polynomialfitofthedensityequationandlotsofparameterizationsfordif-ferentoceanphysicsandsmall-scaleprocessesletthecompleteprogramappearwithawiderangeofdifferentnumericalmethods,thoughflexibleinitsuseduetothemodularformulation.ItiswritteninFORTRAN90andhasgeographicaldomaindecompositioninthehorizontalforMPIparallelization.Traditionallythegridspacelayoutand


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Figure3:Large-scaleimpactoftheAgulhasdynamics.Temperaturesandcurrentsat450mdepthinthehigh-resolutionnestanditsembeddingintheglobalmodel.AsimilarfigurewasthebasisfortheNaturecoverpageonNovember26,2009[8].

theuseoftheverticalaxisinformofvectorsleadtoagoodperformanceonvectorsystems(upto33%ofthepeakperformance).Withabout37×106gridpointsandahightemporal(5-daily)resolutionneededtheoutputofatypi-cal50-yearexperimentiswithmorethan5TBquitelarge.

TheAgulhasmodelisacombinationofacoarse-resolutionglobalbasemodelandahigh-resolutionnestaroundSouthAfrica(Fig.1).Withanominalgridsizeof1/2°thebaseconfigura-tion(ORCA05)successfullysimulatesthelarge-scalewind-drivenandther-mohalinecirculation[3].Itisforcedbyobservedatmosphericconditionsduringtheperiod1958-2004.However,forafullrepresentationoftheAgulhasdynamicsahighspatialresolutionwithgridscaleslessthan10kilometresareneeded;thisisachievedherebynest-inga1/10°gridintothebasemodelusingAGRIF(“AdaptiveGridRefinementInFortran”,[4]).

AGRIFrecombinesthesubroutinesinthemodelcodeviaapreprocessingstepandprovidesroutinesforinter-polationandaveragingbetweenthetwogrids(Fig.2).Itallowsbothmodelstointeractatanygivenbasemodeltimestepwhere(i)thebaseupdatestheboundariesofthenest,(ii)thenestupdatesthecoarsergridpointsofthebase.Duetothedifferentresolutionofphysicalprocessesthenestedmodelhastoperform4-5timestepsbeforethebasemodelissteppedforwardintime.Thiseffectiveandnovel“two-way”nestingapproachassuresthatthissystemnotonlysimulatesthecurrentsystemaroundSouthAfricawithgreatverisimilitude;italsoallowsunravellinghowtheexplicitlysimulatedmesoscalevariabilityintheAgulhasdynamicsfeedsbacktotheglobalocean.

Firstanalyzesaddressedtheimportanceofmesoscaleprocesses,notonlyintherepresentationofthecirculationaroundSouthAfrica[5],butalsointhenetvolumetransferbetweentheIndianandAtlanticOcean(theAgulhasleak-age)[6].Comparisonwiththecoarse-resolutionbasemodelaloneconfirmedthatAgulhasleakageissignificantlyoverestimatedatcoarseresolution,andthereforeincurrentIPCC-typecoupledclimatemodels.TheexplicitsimulationoftheupstreameddiesoriginatingfromtheMozambiqueChannelandeastofMadagascarthatdrifttowardstheAgulhasCurrentanddotriggerthesheddingofAgulhasRings,however,donotmakeasignifi-cantimpactonthevariabilityofAgulhasleakageontimescalesofafewyearsandlonger.

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•ArneBiastoch

Leibniz-InstitutfürMeeres-wissenschaften(IFM-GEOMAR),Kiel

WhatistheeffectoftheAgulhasCurrentsystemonthelarge-scalecirculationintheAtlanticOcean?Comparingthecirculationinsolutionswithandwith-outthehigh-resolutionAgulhasnestallowedidentifyinganintriguingcon-tributionofthemesoscaleAgulhasdy-namicsondecadalcurrentfluctuationsreachingfarintotheNorthAtlantic[7].ThedynamicalsignaloriginatingsouthofAfricarapidlytravelsnorthwardbyboundarywaves.InthetropicalandsubtropicalNorthAtlantictheAgulhas-inducedvariabilityhassimilaramplitudesasthevariabilityintroducedbysubpolardeepwaterformationsevents,amecha-nismthathasbeenknownforitsclimaticimpactandthathasbeenextensivelystudiedinthepast.

InadditiontothedecadalfluctuationsbytheAgulhasmesoscaleanothercli-mate-relevantprocessemergesfromtheAgulhasdynamics.ObservationsreportontheprogressivepolewardmigrationoftheSouthernHemispherewesterlywindsduringthelasttwo-threedecadesandlinkedthosetoanthropogenicforcing.Becauseofthesparseobservationalrecordsithasnotbeenpossibletodeterminewhethertherehasbeenaconcomi-tantresponseofAgulhasleakage.ResultswiththenestedAgulhasmodelshowedthatthetransportofIndianOceanwatersintotheSouthAtlanticviatheAgulhasleakagehasincreasedduringthelastdecadesinre-sponsetothechangeinwindforcing[8].Theincreasedleakagehascon-tributedtotheobservedsalinificationofSouthAtlanticthermoclinewaters.BothmodelandhistoricmeasurementsofSouthAmericasuggestthattheadditionalIndianOceanwatershavebeguntoinvadetheNorthAtlantic,withpotentialimplicationsforastabili-zationofthethermohalinecirculation.

ThefindingshighlighttheimportanceforstudyingtheAgulhasregimeanditsassociatedinteroceanictransportasaprominentkeyregionoftheglobalthermohalinecirculation.

References[1] TheDRAKKARGroup Eddy-PermittingOceanCirculationHind- castsofPastDecades.ClivarExchanges 12,8-10,2007

[2] Madec,G. NEMO=theOPA9oceanengine.Technical report,NoteduPoledemodelisation, InstitutPierreSimonLaplace(IPSL), France,2008

[3] Biastoch,A.,Böning,C.W.,Getzlaff,J., Molines,J.-M.,Madec,G. Causesofinterannual-decadalvariability inthemeridionaloverturningcirculation ofthemid-latitudeNorthAtlanticOcean, J.Climate21,6599-6615,2008

[4] Debreu,L.,Vouland,C.,Blayo,E. AGRIF:AdaptivegridrefinementinFortran, ComputersandGeosciences34,8-13,2008

[5] Biastoch,A.,Beal,L.,Casal,T.G.D., Lutjeharms,J.R.E. VariabilityandcoherenceoftheAgulhas Undercurrentinahigh-resolutionocean generalcirculationmodel,J.Phys. Oceanogr.39,2417-2435,2009

[6] Biastoch,A.,Lutjeharms,J.R.E., Böning,C.W.,Scheinert,M. Mesoscaleperturbationscontrolinter- oceanexchangesouthofAfrica,Geophys. Res.Lett.35,L20602,2008

[7] Biastoch,A.,Böning,C.W., Lutjeharms,J.R.E. Agulhasleakagedynamicsaffectsdecadal variabilityinAtlanticoverturningcirculation, Nature456,489-492,2008

[8] Biastoch,A.,Böning,C.W., Lutjeharms,J.R.E.,Schwarzkopf,F.U. IncreaseinAgulhasleakageduetopole- wardshiftoftheSouthernHemisphere westerlies,Nature462,495-498,2009

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IntheAfricanSudanian(10°N-15°N)andSahelianclimatezones(15°N-18°N)convectivesystemsplayakeyroleinthewatercycle,becausetheycon-tributetoabout80%totheannualrainfall.Theconvectivesystemsarethunderstormcomplexeswithahori-zontalextentofseveralhundredsofkilometers.TheyarepartoftheWestAfricanmonsoon(WAM),whichalsoimpactsthedownstreamtropicalAtlanticbyprovidingtheseedlingdis-turbancesforthemajorityofAtlantictropicalcyclones[1].

TheWAMsystemischaracterizedbytheinteractionoftheAfricaneasterlyjet(AEJ),theAfricaneasterlywaves(AEWs),theSaharanairlayer(SAL),aswellasbythelow-levelmonsoonflow,theHarmattan,andthemeso-scaleconvectivesystems(MCSs).TheAEJcanbeobservedbetween10-12°Nandataheightofabout600hPaandhasatypicalwindspeedofabout12ms-1.TheAEJdevelopsasaresult

ofthereversedmeridionaltemperaturegradientduetotherelativelycoolandmoistmonsoonlayerandthehotanddry

SAL,whichislocatedabovethe

monsoonlayer.

TheAEWsaresynoptic-scaledistur-bancesthatpropagatewestwardsacrosstropicalWestAfricatowardtheeasternAtlanticandtheeasternPacific.TheAEWsarecharacterizedbypropagationspeedsof7-8ms-1,aperiodof2-5days,andawavelengthofabout2,500-4,000km.Importantparametersfortheinitiationofconvec-tionarethespatialdistributionandtemporaldevelopmentofwatervapourintheconvectiveboundarylayer(CBL).Besidesadvectiveprocesses,watervaporismadeavailableintheatmo-spherelocallythroughevapotranspi-rationfromsoilandvegetation.ManyresearchfindingsshowthatthesoilmoistureexertsgreaterinfluenceontheCBLthanvegetation.

InthescopeoftheAfricanMonsoonMultidisciplinaryAnalyses(AMMA)pro-jectweinvestigatethedevelopmentofMCSs,thesensitivityoftheirlifecycletodifferingsurfaceproperties,theroleoflarger-scaleweathersystems(AEWs,theSaharanHeatLow)intheirevolution,andthedevelopmentoftro-picalcyclonesoutofsuchsystems.Inaddition,westudytheinteractionoftheSALwithAfricanmonsoonweathersystems.

TosimulatetheweathersystemsoverWestAfricaweusetheCOSMO(COnsortiumforSmallscaleMOdelling,www.cosmo-model.org)model[2,3].COSMOisanoperationalweatherforecastmodelusedbyseveralEu-ropeanweatherservices,e.g.the

GermanWeatherService(DWD).Additionally,we

Modelling Convection over West Africa


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useCOSMOcoupledwiththeaerosolandreactivetracegasesmodule(COSMO-ART)toinvestigatetheinteractionoftheSALwithWAMsystems.COSMO-ART[4,5]wasdevelopedinKarlsruheandcom-putestheemissionandthetransportofmineraldust.Weusedthecom-puterfacilitiesattheHPXC4000attheSteinbruchCentreforComputing(SCC)astheCOMSOmodelrequiressubstantialsupercomputerresources.Inthefollowing,wefocusontwodif-ferenttopics.OntheonehandthelifecycleofanMCSoverWestAfricaismodelledwithrespecttothesoilcondi-tions.OntheotherhandthefocusliesoncomparisonbetweenthesimulatedMCSsoverWestAfricaandovertheEasternAtlantic.

Thefirstpartofthisstudyinvesti-gatesthesensitivityofthelifecycleofanMCStosurfaceconditions[6].TheanalysisisbasedonsimulationsofarealMCSeventon11June2006whichoccurredinthepre-onsetphaseofmonsoonwhenvegetationcoverislowandtheimpactofsoilmoistureisassumedtobedominant.Differentconditionsforsoilmoisturewereap-pliedforinitializationofthesoilmodelTERRA-ML.TherunbasedontheCOSMOsoiltypedistributionandonoriginalECMWFfieldswasdenotedwithMOI.

However,comparisonwithAMSR-EsatellitedatashowedthattheMOIfieldcontainedtoomuchsoilmoistureintheuppersurfacelayer.Therefore,wereducedthevolumetricsoilmoisturecontentinalllayersby35%compared

totheinitialconditionsofMOI.Thisresultedinasimilarsoilmoisturecon-tentintheuppermostlevelofTERRA-ML,comparedtothesoilmoisturevaluesofabout12%derivedbytheAMSR-Esatelliteandin-situmeasurementsofabout18%fortheuppermost5cmtakenatDano(3°Wand11°N)[7]fortheregionaround11°N,wheretheMCSwasobserved.ThecorrespondingsimulationwasdesignatedasCTRLexperiment.Toeliminatetheeffectofspatialsoilmois-turevariabilityontheinitiationofcon-vection,anadditionalsimulationwithahomogeneous(HOM)distributionofsoilmoistureandsoiltexturewasperformed.InthiscasethevolumetricsoilmoisturewasspecifiedasameanvalueofthevolumetricsoilmoistureintheCTRLexperimentalong11°Nfrom4.5°Wto4.5°E.Toinvestigatetheeffectofdryregionsonconvec-tivesystems,wherethesoilmoisturestructureislesscomplexthanthecon-ditionspresentintheCTRLrun,adrybandof2degreelongitudinalextensionwasinsertedintothehomogeneoussoilmoisturefield.Inthisbandthevol-umetricsoilmoisturewasreducedby35%comparedtothehomogeneousenvironment.Thecorrespondingex-perimentwasdenotedasBAND.


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IntheCTRLcasethreeseparatecellswereinitiatedinthesouth-easternpartofBurkinaFaso.Precipitationofupto6mmh-1wassimulatedat17UTC(Figure1a).Thesouth-westernmostcelldevelopedintheleeofanareawithorographicallyinducedupwardmotion.TriggeringofconvectionoftenoccursinthiswayinWestAfrica.Twofurthercellsdevelopedintheeast,at1.9°Eand11.9°Nandat2.2°Eand11.6°N.InFigure1atheprecipitationpatternoftheCTRLcaseat17UTCisoverlaidonthesoilmoisturedistributionintheuppermostlayerat15UTC.Thisfigureshowsthatallthreecellsdevelopedinthetransitionzonefromawettertodryersurface,whilethecentresoftheprecipitatingcellswerepositionedoverthedryersurface.IncomparisontotheCTRLcase,onlytwoprecipitatingcellshaddevelopedat17UTCintheHOMcase(Figure1b).

ThesetwocellswereobservedatroughlythesamelocationsasthemostintensivecellsintheCTRLcase.However,theprecipitationofbothcellswaslessintensethanthatoftheCTRLcaseatthesametime.IntheMOIcasethefavorableconditionswithhighcon-vectiveavailableenergy(CAPE)andlowconvectiveinhibition(CIN)valuesweremorelimitedinspacethanintheothercases.Inaddition,thesurfacetempera-tureintheregionofinterestintheMOIcasewasabout3°ClowerthanintheCTRLcase.Undertheseconditionsonlyoneweakprecipitatingcellhaddevelopedat17UTC.

Oncetriggered,theconvectivecellsdevelopedquicklyintheCTRLandHOMcaseandmovedwiththeAEJtowardsthewest.Abouttwohoursafteritsini-tiation,thecellshadalreadyorganizedintoanMCSintheCTRLrun.Inthe

Figure2:24-haccumulatedprecipitationinmm(colorshaded)startingfrom06UTCon11June2006(a),Hovmöllerdiagramofprecipitationinmmh-1(colorshaded)averagedbetween10.5°Nand13.5°NonJune11and12,2006(b),andCIN(c)inJkg-1(colorshaded)onJune11,2006at18UTCforBANDcase.Thesolidlinesenframetheareaofthedryband.Takenfrom[6].

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HOMcasethreeseparatecellscouldstillbedistinguishedat19UTC,whichwerelessintensethanintheCTRLcase.TheMOIrunshowedonlyweakconvectiveactivity.Theimpactofthedrybandwithavolumetricmoisturecontentof8.3%,surroundedbyaho-mogeneousmoisturecontentof12.7%onthemodificationofamatureMCSisshowninFigures2aand2b.Precipita-tionwassignificantlyreducedbetween0and2°W,i.e.shiftedbyaboutonedegreetotheeastofthedryband.Precipitationwasinterrupted,whentheMCSapproachedthedryband,butre-generatedwhenthesystemreachedthewesternpartoftheband(Figure2b).Convectionwasalsoiniti-atedinthewesternpartofthedryband(around3°W)atabout19UTC.Thecloudclusterwasaccompaniedbysignificantrainfallandmovedtothewest.At18UTCanareawithlower

CAPEvalueshaddevelopedwithinthedryband,whileCINincreasedtotheeastandwestofit(Figure2c).Eastoftheband(0.83°W,12.2°N)CINfur-therincreasedduringthesubsequenthours.Insidetheband(1.8°W,12.2°N)CINwassignificantlylower.ThelowerCAPEinsidethedrybandresultedmainlythroughlowernear-surfacehumidity.Eastofthedrybandalowernear-surfacetemperatureledtoahigherCIN,whichinhibitedtheconvec-tionoftheapproachingMCS.Thein-creaseofCINinsidethedrybandlaterthatnightyieldedfromthenocturnaldecreaseofnear-surfacetemperatureandthepassageoftheMCSinthesurrounding.

Thesimulationsshowedthatconvec-tionwasinitiatedinallmodelexperi-ments,regardlessoftheinitialsoilmoisturedistribution.Theareawhere

Figure1:Volumetricsoilmoisturein%at15UTCintheuppermostlayer(colorshaded)andprecipitationinmmh-1(isolines,interval2)onJune11,2006at17UTCforCTRL(a),HOM(b),andMOI(c)case.Takenfrom[6].

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convectionwasinitiatedinthesimula-tionscorrespondedroughlywiththeobservations.IntheCTRLcaseallthreecellswereinitiatedalongsoilmoistureinhomogeneitiesandshiftedtowardsthedrysurface.TriggeringofconvectionandoptimalevolutionwassimulatedinareaswithlowCIN,highCAPEandlowsoilmoisturecontent(<15%)orsoilmoistureinhomogene-ities.InCTRLandHOMthecellsde-velopedquicklyandmergedintoorga-nizedmesoscalesystemswhilemovingwestwards.TheMCSintheCTRLrunexperiencedasignificantmodification.Theprecipitationdisappearedwhen

theMCSreachedaregionwhichwascharacterizedbyhighCINvalues(>150Jkg-1),reducedtotalwatercon-tentandsoilmoistureinhomogeneities.

Inconclusion,triggeringofconvec-tiononthisdaywasfavouredbydriersurfacesand/orsoilmoistureinhomo-geneities,whileamaturesystemweak-enedinthevicinityofadriersurface.Thismeansthatapositivefeedbackbetweensoilmoistureandprecipitationexistedforamaturesystemwhereasanegativefeedbackwasfoundfortrig-geringofconvection.Thetwomecha-nismsareillustrativeforthecomplexity

Figure3:ComparisonbetweentheRapidDevelopingThunderstormProduct(RDT)(left)andCOSMOmodelruns(right)onSeptember10,2006at04UTC(upperrow)andonSeptember12,2006at8UTC(lowerrow).Left:ConvectiveobjectsaresuperimposedovertheMeteosatinfraredimagesusingshadingsofgreyupto-65°C,orange–redcolorsbetween-65°and-81°C,andblackabove-81°CintheRDTimages.CourtesyofMétéoFrance.Right:Theverticalintegralofcloudwater,cloudiceandhumidity(kgm-2),indicatingtheconvectiveupdraughtcores,fromthe2.8-kmCOSMOrunwhichwasinitializedonSeptember9,2006at12UTC.Takenfrom[10].

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ofsoil-precipitationfeedbacksinanareawherehighsensitivityofprecipita-tiononsoilmoisturewasproven.

Thesecondstudyinvestigatesconvec-tivesystemsoverWestAfricaandtheeasternAtlantic.TheconvectivesystemsareembeddedintheAEWoutofwhichahurricanedeveloped.IntheafternoonhoursonSeptember9,2006anMCSwasinitiatedoverlandaheadofthetroughofthisAEW.Theconvectivesystemincreasedquicklyinintensityandsizeanddevelopedthreewestwardmovingarc-shapedconvectivesystems(Figure3a,b).Thenear-surfacewindsdepictthemostlywesterlymonsooninflowandtheweakeasterlyinflowintothesystem.Asthesystemdecayed,newconvectiveburstsoccurredintheremainsoftheoldMCS.ThisleadtostructuralchangesoftheformofasqualllinecrossingtheWestAfricancoastlineataroundmidnightonSeptember11,2006.Duringthenext24hours,in-tenseconvectiveburstsoccurredovertheeasternAtlantic.Theseconvectiveburstswereembeddedinacycloniccir-culationwhichintensifiedandbecameatropicaldepressiononSeptember12,2006,12UTCoutofwhichHurricaneHelenedeveloped.Thelargestandlongestlivedconvectiveburstinthisintensificationperiod(Figure3c,d)wasanalyzedhereandcomparedtotheconvectivesystemoverland.Theconvectivesystemsmodifytheiren-vironmentandthesechangescanberelatedtothestructureofthesystemitself.

AseriesofCOSMOrunsoverlargemodeldomains(1000x500gridpoints)with2.8kmhorizontalresolutionwerecarriedoutsuchthatthemodelareawascentredaroundtheMCS.Asthe

systemmovedacrossWestAfricathepositionofthemodelregionwasad-justedforeachsubsequentrun.Thisenabledustoidentifystructuralfea-turesofconvectivesystemsoverWestAfricaandtheEasternAtlantic,andtoanalyzeanddiscusstheirdifferences.Alltherunsare72hinduration.Theparameterizationofconvectionisswitchedoff.Themodelsourcecodewasadaptedtoprovideinformationformoisture,temperatureandmomentumbudgets[8,9,10].

Themodelwasabletocapturetheabovedescribedconvectivesystemsaswellastheirstructureandintensitychangesverywell.WeidentifiedthreestagesinthelifecycleoftheMCSoverWestAfrica:thedeveloping,thema-ture,andthedecayingphase.Toana-lyzethestructureoftheseconvectivesystemsinmoredetail,aneast-westcrosssection(Figure4a)isdrawnthroughtheconvectivesystemshowninFigures3a,b.Duringthematurephase,low-levelconvergenceoccursbetweenastrongwesterlyinflowandaneasterlyinflowfrombehindthesys-temandispartlyenhancedduetothedescendingairfromtherearsystem.Theassociatedascentregionextendsuptoabout200hPaandistiltedeast-wardswithheight(Figure4a).Thecon-vergencecontinuesupto700hPa.Atthisstagestrongdowndraughtshavedevelopedaround7.5°Wjustbehindthelow-levelconvergencezonethatislocatedunderthetiltedupdraughtregion.Thearea

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ofthewesterlyinflowreachesdeepintothesystemuptoabout700hPaandtheAEJhasitsmaximumaround600hPa.Divergenceassociatedwiththeupper-leveloutflowcanbeseenataround200hPawithverystrongeasterlywindsaheadofthesystemandwesterlywindsintherear.Thus,thereisstrongmid-levelconvergenceeastofthetiltedupdraught.

Thecrosssectionthroughaconvec-tivesystemembeddedinthedevel-opingtropicaldepressionovertheeasternAtlanticisshowninFigure4b.AmajordifferencebetweentheMCSoverWestAfricaandtheconvectiveburststhatareembeddedinthecircu-lationovertheAtlanticistheirlifetime.TheMCSoverlandlastedforabout3daysandthesuccessionofMCSsovertheoceanonlyforabout6to24hours.Theregionofmaximumheat-ingandascentisverticalandnottiltedasintheconvectivesystemoverland.TheAEJseemstooccurataround700hPawestoftheconvectivesys-

tem.Thisisabout100hPalowerthanfortheMCSoverWestAfricawheretheairisacceleratedduetoairthatexitedtheupdraughtcoreatlowerlev-elsandslightlydescends.Itisalsoap-parentfromthecrosssectionthatthedowndraughtsarenotasstrongasfortheMCSoverland.Furthermore,thelow-levelconvergencecoversamuchbroaderregionthanoverthecontinent.

Toquantifythedifferencebetweentheconvectivesystemsoverlandandoverwaterandtoassesstheinflu-enceoftheconvectivesystemsontheenvironment,potentialtemperature,relativevorticity,andpotentialvorticitybudgetsforregionsencompassingtheconvectiveregionwerecalculated.De-tailsandtheresultsforthebudgetcal-culationscanbefoundin[10].Infuturestudiestheanalysiscouldbeappliedtoothercasesinordertogeneralisetheseresults.

Figure4:Crosssectionsthroughtheconvectivesystemoverland(a)andovertheocean(b).Theverticalvelocity(shaded)andzonalwind(contourintervalis3ms-1)areshown.Thecrosssection(a)isalong13.1°NonSeptember10,2006,05UTC,and(b)along12.8°NonSeptember12,2006,09UTC.Takenfrom[10].

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• Juliane Schwendike• Leonhard Gantner• NorbertKalthoff• SarahJones

Institutfür Meteorologieund Klimaforschung, Karlsruher Institutfür Technologie

AcknowledgmentThisprojectreceivedsupportfromtheAMMA-EUproject.BasedonaFrenchinitiative,AMMAwasbuiltbyaninter-nationalscientificgroupandfundedbyalargenumberofagencies,especiallyfromFrance,UK,USandAfrica.Ithasbeenthebeneficiaryofamajorfinan-cialcontributionfromtheEuropeanCommunity’sSixthFrameworkRe-searchProgram.

Detailedinformationonscientificcoor-dinationandfundingisavailableontheAMMAInternationalwebsite:http://www.amma-international.org

References[1] Avila,L.A.,Pasch,R.J.

“AtlanticTropicalSystemsof1993”,MonthlyWeatherReview,123:887-896,1995

[2] Steppeler,J.,Doms,G.,Schättler,U., Bitzer,H.W.,Gassmann,A., Damrath,U.,GregoricG.

“Meso-gammaScaleForecastsusingtheNonhydrostaticModelLM”,MeteorologyandAtmosphericPhysics,82:75–97,2003

[3] Schättler,U.,Doms,G.,Schraff,C.“ADescriptionoftheNonhydrostaticRegionalModelLM”,partVII:User’sguide,DeutscherWetterdienst,www.cosmo-model.org,2008

[4] Vogel,B.,Hoose,C.,Vogel,H., Kottmeier,C.

“AModelofDustTransportAplliedtotheDeadSeaArea”,MeteorologischeZeitschrift,15:DOI:10.1127/0941–2948/2006/0168,2006

[5] Vogel,B.,Vogel,H.,Bäumer,D., Bangert,M.,Lundgren,K.,Rinke,R., Stanelle,T.

“TheComprehensiveModelSystemCOSMO-ART–RadiativeImpactofAerosolontheStateoftheAtmosphereontheRegionalScale”,AtmosphericChemistryandPhysics,9:14483–14528,2009

[6] Gantner,L.,Kalthoff,N.“SensitivityofaModelledLifeCycleofaMesoscaleConvectiveSystemtoSoilConditionsoverWestAfrica”,QuarterlyJournaloftheRoyalMeteorologicalSociety,136(s1):471-482,2010

[7] Kohler,M.,Kalthoff,N.,Kottmeier,C.“TheImpactofSoilMoistureModificationsonCBLCharacteristicsinWestAfrica:ACase-StudyfromtheAMMACampaign”,QuarterlyJournaloftheRoyalMeteorologi-calSociety,136(s1):442-455-,2010

[8] Grams,C.M.“TheAtlanticInflow:Atmosphere-land-oceanInteractionattheSouthWesternEdgeoftheSaharanHeatLow”,Master’sthesis,InstitutfürMeteorologieundKlimaforschung,UniversitätKarlsruhe,Karlsruhe,Germany,March2008

[9] Grams,C.M.,Jones,S.C., Marsham,J.H.,Parker,D.J., Haywood,J.M.,Heuveline,V.

“TheAtlanticInflowtotheSaharanHeatLow:ObservationsandModelling”,Quar-terlyJournaloftheRoyalMeteorologicalSociety,136(s1):125-140,2010

[10]Schwendike,J.,Jones,S.C.“ConvectioninanAfricanEasterlyWaveoverWestAfricaandtheEasternAtlan-tic”:aModelCaseStudyofHelene(2006).QuarterlyJournaloftheRoyalMeteorolo-gicalSociety,136(s1):364-396,2010

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Extragalacticjetsareamongstthemostspectacularphenomenainastrophysics:thesedilutebuthighlyenergeticbeamsofplasmaareformedintheenvironsofactiveblackholes,movingwithspeedsverynearthespeedoflight.Theyrunintothehotgassurroundingthegalaxy,wheretheyareeventuallydeceleratedandheatupthegasconsiderably,dig-ginglargecavities(theso-calledjetco-coon)intotheextragalacticgas.

Boththejetsandthecavitiesareob-servabletodaywithradioandX-raytelescopesonearthorinspace.Theseobservationshaverevealedthatthepowerofthesejetsisevengreaterthanwasthoughtbefore:morethanonetrilliontimesthetotalpowerofoursun(1039watts)forthemostpower-fulones–withactivitydurationsofsometenmillionyears.Thispowerultimatelyoriginatesfromtheactivesupermassiveblackholeofthegalaxy,sincethejettapstheenormousro-tationalenergyoftheblackhole.This

hugeamountofenergyclearlyhasaconsiderableimpactontheenergybudgetoftherespectivegalaxyanditsenvironment.Sinceextragalacticjetsaremostfrequentinthemostmas-sivegalaxies(observedinroughlyeverythirdamongthem),theyhavebecomeacommonexplanationforcosmo-logicalsimulationsfailingtoreproducegiantgalaxiescorrectly,despitetheirotherwisegreatsuccessinmodelingtheevolutionofouruniverseanditsgalaxies.Whileastronomicalobserva-tionsshowthatgiantellipticalgalaxiesconsistofoldandredstars,showingalmostnosignsofcurrentformationofnewstars,thesegalaxiesstillgrowincosmologicalsimulationsandthereforearemuchbluer,activelyformingstarsandhaveconsiderablygreatermasses.Thishascausedanincreasedinterestinjetphysicsandinresearchprojectsfocusingontheinteractionofjetswiththeirhostgalaxyanditsenvironment,nowreferredtoas“jetfeedback”.Yet,thedetailedprocessesinvolvedand

Figure1:Velocityfieldofajet(densitycontrast0.001)after30millionyears.Thejetbeam(inred)reachesouttothejetheadandthenturnsback,inflatingaturbulentcocoon(mostlygreen).

The Maturing of Giant Galaxies by Black Hole Activity


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theexactimportanceofjetsisstillun-known.Thisbecomesobviousintwoopposingprocessesthatareinvokedinthecurrentliterature:positiveandnegativefeedback.

ThinkPositive–orNegative?Denseandverycoolcloudsofgasarethebirthplacesofstarssinceonlythencangravitysurmountthermalpressureandcausethecloudstocollapse,ulti-matelyformingnewstarsasnuclearfu-sionsetsin.Ifcloudsarehitbyajetoritsprecedingbowshock,theyarecom-pressedbytheimpactandcanbecomegravitationallyunstable.Thiswouldresultinanincreasedrateofstarformation(positivefeedback).Ontheotherhand,thesameimpactcouldaswelldisruptthecoldcloudsandtherebydestroytheseedsnecessaryfortheformationofnewstars(negativefeed-back).Additionally,thejetwouldheatupthehotgassurroundingthegalaxytohighertemperaturesandpreventitfromcoolingdown,gettingcompressedandformingnewcoldclouds.Bothpro-cesseshavebeendemonstratedtobepossible,andonlyadetailedstudywitharealisticunderlyingmodelcanhelpusrevealtheirrealimportance.

Observationsofdistantradiogalaxies,showingthemastheywere10billionyearsago,giveadditionalhintsontheinteractionbetweenthejetandthedisk.There,extendedemission-linenebulaealignedwiththejetaxisareobservedanditisconjecturedthatthesenebulaeareactuallycreatedbytheinteractionofthejetwithagalaxy'sgaseousdisk.

ComputationalChallengesWehaveconductedaseriesofmagne-tohydrodynamicjetsimulationstoex-aminetheinteractionofjetswiththeirenvironmentatveryhighresolution

assumingaxisymmetry.Thesecompu-tationsareextremelydemandingifre-alisticparametersareused:althoughthejetplasmamoveswithalmostthespeedoflight,itsdensityismuchlowerthantheambientdensityoftheextra-galacticgas(inoursimulationstypically1,000timessmaller),whichresultsinamuchslowerpropagationofthejet.Also,aconsiderablerangeofspatialscaleshastobecovered.Whilethejetsextendovermorethan100kpc(1kpc=3x1016km),thejetbeamsare100timesnarrowerandhavetoberesolvedindetail.Allthisresultsinsimulationswithmorethan6millioncellsandmorethanonemilliontimestepsnecessary.Theverylargenum-beroftimestepsmakessimulationstime-consumingbutdoesnotallowamassivelyparallelapproachsincethe“problemperprocessor”wouldbecometoosmallthenandcommunicationcostsbetweenprocessorswouldbe-comeunwieldy.Thesesimulationswere

Figure2:3Dsimulationshowingtheinteractionofajetandagalacticgaseousdisk(volumerenderingofdensity)

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thereforeonlypossibleonapowerfulsupercomputersuchastheNECSX-8attheHighPerformanceComputingCenterStuttgart(HLRS),towhichwehaveadjustedandoptimizedourcode.Duetoitsvectorcapabilitiesandsharedmemoryarchitecturepernode,thecoderanveryefficientlyandallowedustoreachrealisticsizesforthejets.

Wefoundthattheexpansionofthejetcocoonfollowsself-similarmodelsonlyatearlytimes,butdeviatesfromthatsignificantlyoncethecocoonap-proachespressurebalancewithitsenvironment.Wefoundthistobeconsistentwithobservationsandalsoachievedbowshockshapesandstrengthssuchasthoseobservedtypically.Magneticfieldsturnedouttobeimportanttostabilizethecontactsurfacebetweenthejetandtheambi-entmediumandweremoreoverfoundtobeefficientlyamplifiedbyashearingmechanisminthejethead.

TheMistofDistantGalaxiesWithrespecttotheriddleoftheemission-linenebulae,wewereabletotesttwomodelsforthelocationandkinematicsoftheline-emittingcloudsagainstobservedpropertiesandfoundthatcloudsembeddedinthejetcocoonwereabletomuchbetterreproduceobservedvelocitiesandmorphologiesthancloudsembeddedintheshockedambientgas.

About10percentofthetotaljetpowerwasmeasuredaskineticenergyinthejetcocoon,whichmadeitpossibletolinkthesefindingstosimulationsofmulti-phaseturbulencethatmodeltheamountsandemissionpowerofthecoolgasphaseembeddedinthesetur-bulentregions.Theexpectedemissionlineluminosities,however,disagreeconsiderablywiththeobservedrangeofluminositiesandweconcludedthatthemodelsarestilltoosimpletoin-cludeallthenecessaryphysics.Thiswas,admittedly,nottoosurprisingsincethemodelsreliedoncloudspas-sivelyadvectedwithandspreadallacrossthecocoonplasma,whiletheinteractionofrealcloudsofcoldgaswouldbeconsiderablymorecomplex.

Movingon–Simulationsin3DToimproveourmodelofthejet–cloudinteraction,wehadtoextendoursimu-lationstofullthreedimensionssinceaclumpygalacticorintergalacticgascannotbemodeledproperlywithinaxi-symmetry.Thisalsomadeitnecessarytomovetoanothercode:RAMSES,anadaptivemeshrefinementcode,thatin-cludescooling,gravity,starformation,cosmologicalevolutionandmagneticfields.Themeshrefinementishighlysuitableforresolvingsmallcloudsinanotherwiselargecomputationaldomain,

Figure3:Thegaseouscloudsinthedisk(green)arecompressed(blue)bytheactionofthejetandtheremaininggasispushedoutwards.

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• VolkerGaibler

Max-Planck- Institutfür extraterres- trischePhysik, Garching

incontrasttoauniformlyresolvedmesh.Thecurrentimplementationallowsamaximumdynamicrangeinlengthscaleof6ordersofmagnitude.ThecodeisparallelizedbyMPIandin-cludesdynamicload-balancingbetweenthedifferentMPIprocesses.The3Dsimulationsareclearlycomputationallyverydemanding,sinceanadditionaldimensionresultsinamuchlargernumberofcells,eveniftheresolutionissomewhatlower.Ontheotherhand,ahighernumberofcells(incontrasttotimesteps)generallycanbehandledbymassiveparallelizationifanefficientmethodisused.

OutlookWehavesuccessfullytestedRAMSESonboththeNehalemclusterattheHLRSandtheHLRB-IIattheLeibniz-Rechenzentrum(LRZ),andfoundittobescalingalmostlinearlywithupto4,000cores,ifsufficientbandwidthisavailable.OntheNehalemcluster,theperformancewas~50percentsmallerthanexpectedfromthesingle-coreperformancesincememorybandwidthbecomeabottleneckformorethan4corespernode;scalingthenincreasedalmostlinearlyforalargernumberofnodes.HLRB-II,however,didnotsufferfromthisandshowedexcellentscalingbehaviour.WeconjecturethatthememorybandwidthbottleneckontheNehalemclustermayberelatedtode-tailsoftheMPIimplementation,sinceMPIadjustmentsonalocalHarper-townBeowulfclusterwereabletogetaroundthislimitation.

Forasmalltestsimulation,wehavesuccessfullysetupaclumpygalacticgaseousdisk,similartodisksactuallyobservedindistantgalaxies.Ajetispositionedinthecenterofthegalaxyandinteractionofthejetwiththecold

gascloudsembeddedintheambientmediumiscomputedexplicitly.Prelimi-naryresultsindicatethatactuallybothpositivefeedbackbycompressionofcloudsinthecenteraswellasnega-tivefeedbackbyremovalofgasalongthejetaxismaybeactingatthesametime.Sofar,thesimulationshaveonlycoveredatimemuchshorterthanourprevioussimulations,butrunsonalargenumberofprocessorswillallowustoeventuallyexaminetheactionofjetfeedbackindetailandoverrealisti-callylongtimes.

References[1] Gaibler,V.,Krause,M.,Camenzind,M. “VeryLightMagnetizedJetsonLarge Scales–I.EvolutionandMagneticFields”, MonthlyNoticesoftheRoyalAstronomical Society,400,pp.1782-1802,2009

[2] Krause,M.,Gaibler,V. “PhysicsandFateofJetRelated EmissionLineRegions”,Conference contribution,atarXiv:0906.2122

[3] Gaibler,V.,Camenzind,M. “NumericalModelsforEmissionLine NebulaeinHighRedshiftRadioGalaxies”, WolfgangE.Nagel,DietmarB.Kröner, MichaelM.Resch(Eds.),Springer2010, HighPerformanceComputinginScience andEngineering‘09

[4] Gaibler,V. “VeryLightJetswithMagneticFields”, PhDThesis,Ruprecht-Karls-Universität Heidelberg,2008

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Thevalueofleadingedgehighperfor-mancecomputingsystemscanonlyberevealediftheprogrammingenviron-mentforapplicationdevelopersallowstherealizationofefficientsimulationprograms.Usersofsuchapplicationsexpectareliableandrobustoperatingenvironmentwithagoodratiobetweenperformanceandenergycosts.

Thefastdevelopmentonthehardwaresectorandthecomplexityofthetasktoexploitmoreandmorecomputingcoresforsolvingincreasinglylargeproblemsorsolvethesameprobleminshortertimecannotbeaddressedwithcommodityoftheshelfproductsbutrequiresintensiveresearchactivitiesinclosecollaborationofapplicationdevel-opers,hardwarevendorsandsuper-computingcentressuchasHLRS.ConsequentlyHLRSisinvolvedinawiderangeofresearchactivitiesandhasestablishedclosevendorcollaborationssuchastheTeraflopWorkbenchandtheCrayExascaleResearchCentre.

StartingfromprojectsprovidingtheinfrastructuresuchasthePartnershipforAdvancedComputinginEurope(PRACE),VISIONAIRorHPC-Europa,moreshorttermorientedresearchprojectsaimtorealizeanefficientoperatingenvironment.AsexampletheprojectsGAMESandCoolEmAlldevelopindicators,methodsandsolutionsforrealizingenergyefficientdatacentres.Realizingreliableserviceprovisiondemandsforcontrolledprocessesandmanagedinfrastructures.TheplugITprojecthasbeenaddressingtheneedforalignmentofbusinessgoalsandtheITinfrastructurelayer.

Anotherimportantpartoftheresearchactivitiesisfocusedondevelopertools.HLRSisnotonlyinvolvedintheMPIstandardizationprocessandanactivedeveloperoftheOpenSourcereferenceimplementationOpenMPI,buthasdevelopedinnovativedebuggingtoolssuchasTemanejoforemergingnewtaskbasedprogrammingmodels.

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•StefanWesner,


Themajorchallengeforthenexteighttotenyearsistofindsolutionsforexascalecomputingsystems.Thesignificantchangesonthehardwareleveldemandforradicallynewapproachesandanevenclosercollaborationwithapplicationdevelopers.HLRSisaleadingpartnerinCRESTA(CollaborativeResearchintoExascaleSystemware,ToolsandApplications),oneofthethreeexascaleprojectsattheEuropeanlevel.Inthislargescaleprojectallaspectsoftheproblemfromnewmathematicalmodelsandapproachesoverapplicationsandlibrariesdowntothehardwarelevelareinvesti-gated.AnotherexascaleprojectofHLRSisTEXT(TowardsEXascaleApplicaTions)validatinghowhybridcomputingapproachesmixingexistingandnewprogrammingmodelssupportincreasedscalability.

TheexamplesonthefollowingpageswerechosenfromourjournalinSiDEandcanonlypresentashallowimpressionoftheresultsachievedinmorethan40currentlyrunningresearchprojectswithmorethan60researchersatHLRSworkingontheseprojectsonstate,national,andEuropeanlevel.

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Figure1:CoolEmAllConcept

ITinfrastructuresareresponsibleforatleast2%oftheglobalenergyconsump-tionmakingitequaltothedemandoftheaviationindustry.Furthermore,inmanycurrentdatacentrestheITequipmentusesonlyabouthalfofthetotalenergyforcomputing,whilstmostoftheremainingenergyisrequiredforcoolingandairmovement.ThisoftenresultsinpoorPowerUsageEffec-tiveness(PUE)valuesandsignificantCO2emissions.Forthisreasonissuesrelatedtocooling,heattransfer,andITinfrastructurelocationaregainingmoreattentionandarecarefullystudiedduringplanningandoperationofdatacentres.

Inthiscontext,theconstructionofdatacentresbyusingmodularbuildingblocksisgainingmoreandmoreattention,inparticularasapotentialantipoletospecializedfacilities.Thismodularapproachisbecomingincreasinglypopularduetoflexibilityofdesign,lowercostsandshorterbuildingtimes.Thismodularapproachcanrefertoavarietyofapproaches-oneofthemostpopulararedatacentreshousedinstandardshippingcontainers.Inaddition,thismodularapproachcanalsorefertopre-configuredunitswhicharejoinedtogethertobuild-uplargecomputingfacilities,withe.g.,hundredsofsquaremetersinsize.

CoolEmAll - Platform for Optimizing the Design, Operation and Cooling of modular configurable IT Infrastructures

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blocks,andenergyre-usedbyfacilitiesconnectedtoITinfrastructuresareallcrucialtounderstandandimprovetheenergyefficiencyofdatacentresasawhole.Tocarefullystudytheseissues,simulation,visualization,anddecisionsupportingtoolsareneeded,supportingtheoptimizationofthedesignandopera-tionofnewenergy-efficientmodularITinfrastructuresandfacilities.

ToaddresstheaforementionedITenergyefficiencyissues,themaingoalofCoolEmAllistoprovideadvancedsimu-lation,visualizationanddecisionsupporttoolsalongwithblueprintsofcomputingbuildingblocksformodulardatacentreenvironments.Oncedeveloped,thesetoolsandblueprintsaregoingtoallowtominimizetheenergyconsumption,andconsequentlytheCO2emissionsoftheentireITinfrastructure,takingintoaccountthecorrespondingfacilitiesaswell.Thiswillbeachievedby:

1.thedesignofdiversetypesofmodularcomputingbuildingblocks(ComputeBoxBlueprints),whicharegoingtobewelldefinedbyenergyefficiencymetrics

2.thedevelopmentofasimulation,visualizationanddecisionsupporttoolkit(SVDToolkit)thatwillenabletheanalysisandoptimizationofITinfrastructuresassembledwiththesebuildingblocks.

Therefore,thesemodularcomputingmodulesaswellasthetoolkitaregoingtotakeintoaccountthreeaspectsreflectingthemajorimpactonactualenergyconsumption,namelythecoolingmodel,theaccordingapplicationpro-pertiesandworkloads,aswellaswork-loadandresourcemanagementpolicies.

Tothisend,theenergyefficiencyofcom-putingbuildingblockswillbepreciselydefinedbyasetofnovelmetricsex-

However,whilespecialisedfacilitiesareestablishedincurrentenvironments,thereisasignificantneedtoanalysetheenergyefficiencyaspectsofsuchamodularapproachinordertoallowforacomparisonoftheseapproaches.Inparticular,adeepinsightintothetotalenergyconsumptionofboth,largedatacentresandsmallerfacilities,enforceadditionalresearchtodeterminehowefficientthisapproachis.Animportantaspectwhenconsideringtheenergyefficiencyofmodulardatacentresisthecoolingtechnique.Theuseofap-proachessuchas“freeaircooling”whereexternalairisusedtocoolsystemsratherthanelectricalchillerscanhelptoimproveefficiencyandachievePUEratingsclosetotheidealof1.0.

Thecoolingandheattransferprocessesarenottheonlyimportantaspectsinfluencingtheenergyefficiencyofdatacentres.ActualpowerusageandeffectivenessofenergysavingmethodsheavilydependsonthetypesofITapplicationsandworkloadproperties.However,totakefulladvantageofthesemethods,

(i) applicationpowerusageand performancemustbemonitored inafine-grainedmanner,and

(ii)parametersandmetricscharacter- isingboth,applicationsandresources, mustbepreciselydefined.

Consequently,thereisalargeamountofparametersimpactingtheenergyefficiencyofITinfrastructures.Alltheseparametersshouldbetakenintoaccountduringthedesignandconfigurationofdatacentres.Issuessuchastypesandparametersofapplications,workloadandresourcemanagementpolicybasedscheduling,hardwareconfiguration,metricsdefiningefficiencyofbuilding

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Figure2:Airflowvisualisationinadatacentre

pressingrelationsbetweentheenergyefficiencyandessentialfactorslistedabove.Inadditiontocommonstaticapproaches,theCoolEmAllplatformwillenablestudiesofdynamicstatesofITinfrastructuresbasedonchangingwork-loads,managementpolicies,coolingmethod,andambienttemperature.ThemainconceptoftheprojectispresentedinFigure1.

Therefore,CoolEmAllisgoingtoaddressthefollowingtechnicalobjectives:

1. Thedevelopmentofasimulation,visualization,anddecisionsupporttool-kit(SVDToolkit),allowingforanalysinganddesigningmodularITinfrastructuresandfacilitieswithresource-efficientcooling.ThisplatformwillsupportITinfrastructuredesigners,decisionmakersandadministratorsintheprocessofplanningnewinfrastructuresorimprovingexistingones,likeexemplaryshowninFigure2.TheintendedmodularapproachtobuildandmodelITinfra-structuresandfacilitiesallowsformanyextensionpossibilitiesandhighlevelofcustomization.CoolEmAllwilldevelopa

flexiblesimulationplatformintegratingmodelsofapplications,workloadschedulingpolicies,hardwarecharacter-istics,coolingandairandthermalflowsusingComputationalFluidDynamics(CFD)simulationtools.Theflexibilityofthesemodels,basedonmodelparam-etersettings,willensureflexibilityoftheentireCoolEmAllSVDplatform,allowingcapturingrequiredmodelsettingsandsimulatethesemodelsforawiderangeofapplications,workloadschedulingpolicies,IT-Infrastructureandhardwarecharacteristics.AdvancedvisualizationtoolsanduserinterfaceswillallowuserstoeasilyanalysevariousoptionsandoptimizeenergyefficiencyofplannedITinfrastructuresandfacilities,asshowninFigure3.

2.Theprovisioningofblueprintsofcomputingmodulesandabasicproto-type.Thebasicversionofthismodulewillenabletestsandreal-lifeexperimentsprovidingrealisticbehaviouralinforma-tionforthesimulationmodels,allowingcapturingthermalandenergyefficiencybehaviouronnodeandracklevel,andwillalsoenabletheverificationofthese

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Figure3:VisualisationWorkflow

virtualizationandHPCapplications.Theproposedpolicieswillbeappliedinsimulationstostudytheirimpactonenergy-efficiencyindiverseconfigura-tionsandinlargescale.

4.Theanalysisandparameterizationofapplicationsandworkloads.TheCoolEmAllsimulationsaswellaswork-loadmanagementtechniqueswilltakeintoaccountspecificworkloadandapplicationcharacteristics.Tothisend,CoolEmAllwillpreparebenchmarksandclassificationofapplicationsandwork-loads.Thisknowledgeaboutapplicationpropertieswillbeusedto(i)simulatetheirimpactonthermalissuesandenergyefficiencyand(ii)toproposethermalawaremanagementpolicies.

5.Thedefinitionofspecificenergyefficiencymetrics.Precisedefinitionsofmetricsexpressingtrade-offsbetweenenergyandperformancewillbedefined.Thesemetricswillgobeyondexistingones(e.g.thosedefinedintheCodeofConductonDataCentersEnergyEf-ficiency).Withthisrespect,CoolEmAllisgoingtotakeintoaccountmetrics

models.Thisprototypewillincludefine-grainedmonitoringcapabilities,allowingforadetailedinspectionoftheentireenvironment.Basedonthisevaluation,arefinedandoptimizedprototypewillbedesignedfordiversescenariosinclud-ingvarioushardwaredensities,coolingmethods,workloadsandrequirements(Figure4).

3. Thedefinitionandevaluationofthermal-andenergy-awareworkloadschedulingandresourcemanagementpolicies.Theproposedpolicieswillincludeintelligentworkloadschedulingandresourcemanage-ment(e.g.dynamicswitchingoffnodes,loweringfrequencyandvoltagetoavoidexcessiveheatgeneration).Thecor-respondingdecisionswillbebasedonfine-grainedhardwareandapplicationmonitoring.Theselectionandsetupofthecorrespondinghardwarewilldependonapplicationstypes,workloadrequire-ments,coolingmethod,andambienttemperatures.InordertoreflecttheevaluationoftheCoolEmAllapproachwithinarealisticenvironment,twomajortypesofworkloadwillbeconsidered:datacentrecloudworkloadsusing

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Figure4:SVDArchitecture

definedbyotherprojectsaswell,extendthemorproposeadditionalmetricsexpressingclassesofefficiencyincludingrelationbetweenenergyefficiencyandapplicationcharacteristics,workloadproperties,ambienttemperatures,requiredheatre-useefficiency,etc.Inparticular,themetricsdefinedandevaluatedwithintheGAMESproject[1]aregoingtobetakenintoaccount.

6.Verificationofsimulationtoolsandtheirapplicationforspecificscenarios.Theverificationoftheproposedmethodsandsoftwarewillbeperformedbytestsinrealenvironmentsusingabasicprototypemodule,realapplications,aswellasenhancedmonitoringsystemsbasedonsensors.CoolEmAllwillalsoperformcoupledsimulationsforseveraldiversesettingsincludinglargescaleITinfrastructuressuchaswholedatacentres.Thesesimulationswillusecollectedtraces(e.g.fromtheEUproject–GAMESorpartners)toplan,designandoperatenewandexistingITinfrastruc-turesandfacilities.Inthisway,thefinalblueprintsofthecomputingmoduleswillbeevaluatedandoptimizedinspecificsettings.

SimulationandvisualizationtechnologiesareessentialpillarsoftheCoolEmAllconcept,asitallowstoleverageandexplorenewdatacentrearrangementsandsolutions.Therefore,theopensourceCFDpackagecalledOpenFoam[3]andtheCOVISEsoftware[4],developedbyHLRS,willbetheintegrationandenhancedtoprovideareal-timeCFDmodellingcapability.Theresultingpackageenablesintegrationofcollectedoperationaldataintoasimulationtoachieveoptimalenergy-efficientandthermal-awaredesignofdatacentresconsistingofmodularcomputingunits.ThefirstprototypesofthesemodularcomputingunitsaregoingtobedevelopedbasedontheexistingexperienceofChristmannwiththeirRECS(ResourceEfficientComputingSystem[2]).

TheuseofthesemodularcomputeunitsisentirelyinlinewithCoolEmAll’sresearchintoimpactofcoolingsolutionsontheenergyefficiencyofITinfra-structures–inparticularleveragingoutsideairventilationforcoolingwithoutartificialequipmentaswellasreusingwasteheatgeneratedbycomputation.Themodularcomputeunitsdeveloped

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60

•AlexanderKipp•UweWössner Universityof Stuttgart,HLRS

schedulingofworkloads,takingadvantageofcoldermachinesorspecifichardwarebestsuitedforagivenjob,cansignifi-cantlyinfluencetheairflowinsideadatacentre,reducingoreveneliminatingtheneedforartificialcooling.TheimpactoftheworkloadmanagementbasedontheSVDToolkit-designeddatacentresemphasisestheCoolEmAll’sgoalofaholisticapproachtonextgenerationgreendatacentres.

HLRScontributestoCoolEmAllbyactingasthetechnicalmanagerofthisprojectaswellassimulationandvisualizationexpert.Inparticular,HLRSisgoingtocoordinatethedesignandrealizationoftheSVD-platformandcontributewithitsbroadexperienceinsystem-monitor-ingandmanagementexpertise.Finally,HLRSisgoingtocontributewithitsknowledgeinthedefinitionandanalysisofenergy-efficiencyrelatedmetrics.

Participants• InstytutChemiiBioorganicznej PAN,PL• HighPerformanceComputing CentreStuttgart(HLRS),D• UniversitePaulSabatier,F• ChristmannInformationstechnik+ MedienGmbH&Co.KG,D• The451GroupLTD,UK• InstitutdeRercercaenEnergiade Catalunya,E• AtosOrigin,E

References[1] http://www.green-datacenters.eu[2] http://shared.christmann.info/download/ project-recs.pdf[3] http://www.openfoam.com/[4] http://www.hlrs.de/organization/av/vis/ covise

aspartofCoolEmAllcanbeusedinasymbiosisdeploymentscenario,wherecomputingfacilitiesbenefitotherbuild-ingsthatsurroundthem,resultinginimprovedoverallenergy-efficiencyofanurbanarea.AnotherresultoftheCoolEmAllresearchandsimulationsofthemodularcomputeunitdesignanddeploymentscoulddirectlybenefitthefieldofhigh-densityserverracksbymodellingtheairflowaroundthemandhelpinfindingsolutionsforthecoolingproblemofdenseHPCdatacentres.Bothmodularhigh-densitydatacentresandsymbiosisdeploymentsdirectlybenefitfromtheSVDToolkit(simulation,visualizationanddecisionsupport),whichisoneofthemajoroutcomesoftheCoolEmAllproject.

Software,especiallyapplicationsplaysignificantrolesbothintermsoftheperformanceandenergy-efficiencyofcomputations.Therefore,theCoolEmAllprojectaimstoenhanceexistingandtodevelopandstandardizenovelfine-grainedenergyandthermal-awareap-plicationandhardwaremetrics.Thesewilltakeintoaccountboth,theenergybudgetandthermalairflowimpactsofanapplicationrunningonaparticularhardware.Duetothegranularityofthesemetrics,theexistingmonitoringplatformsareinsufficientduetoexces-sivebandwidthandprocessingpowerrequirements,thusCoolEmAllwilldevelopnewmonitoringsolutionstofacetheproblemfornextgenerationgreenITinfrastructure.

Giventhesemetricsandprovidedafine-grainedmonitoringofbothhard-wareandsoftware,CoolEmAllisgoingtoadvancethefieldofclusterschedul-ingwithnewworkloadmanagementalgorithmsandpoliciesleveragingappli-cationcharacteristicsandenergyandthermalmeasurements.Proper

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VISIONAIRisestablishingaEuropeaninfrastructureforhighlevelvisualizationfacilitiesthatisopentoresearchcom-munitiesacrossEuropeandaroundtheworld.Byintegratingexistingfacilitiestoapan-Europeannetwork,itwillcreateaworld-classresearchinfrastructureenablingtoconductcutting-edgeresearch.Currentscientificchallengessuchasclimateevolution,environ-mentalrisks,molecularbiology,health,andenergyrequirethemanagementofincreasinglycomplexandvoluminousinformation,thuscallingforthedevelop-mentofevermorepowerfulvisualizationmethodsandtools.OnmanysitesacrossEurope,itisinfeasibletofundthenecessaryvisualizationfacilitiesthatareneededtotacklehighfidelity,largescreenand/orimmersivevisualization.VISIONAIRistargetedtofillinthisgapbyprovidingaccessto

thepartnerfacilities,openingitsdoorsforinterestedre-searcherstousethemultitudeofser-vicesavailableacrosstheEuropeanvisu-alizationfacilities.Aftersubmittingasuccessfulresearchproposal,interna-tionalresearchers

areinvitedtovisitthepartnerfa-cilityoftheir

choiceorwhatfitsbesttothescien-tificgoalstheyhaveinmindtoconducttheirresearch.Theyarenotonlygivenaccesstothetopvisualizationfacili-tiesinEurope,butarealsosupportedintheirresearchbyfundingtheirlivingandtravelexpenses.Researcherscanchoosefromover20facilitieslocatedin12countriesinEuropeandIsrael.

Theprojecttargetsdifferentfieldsofvisualization.ScientificVisualizationoffersaccesstomethods,softwareandhardwareneededforsuccessfullyvisualisingscientificdata,including-butnotlimited-toengineering,medicalvisualization,biology,chemistryandphysics.Ultra-High-Definitionfacilitiesconnectedbyhighspeednetworksaretargetedatusersthatwanttocreatehighresolution,highqualityimages(upto8k)andaccessthosebyhighspeednetworks.VISIONAIRprovidesthehardwareandtheuniquenetworkdis-tributionservicesneededfortrans-missionoftheimagestotheirend-points.Thenetworkservicesenablemultiplehigh-resolutiondigital-mediastreamstobetransportedamongglobalsites,usingdynamicallyprovisionedopticallightpathsacrossmultipledomains,whichcanbeusedonascheduledoron-demandbasis.WhileScientificandUltra-High-DefinitionVisualizationcanbedoneinanyenvironments,researchersspecificallytargetingVRcanalsoapplyatamultitudeoffacilities.Here,thefocusisonimmer-sive-possiblyalsohaptic-experiencesinvirtualenvironments.Equipmentavailableforresearchersrangesfrom

headmounteddisplaystofullyfledgedstereoscopicPower-

WallsandCAVEs.Further

VISIONAIR

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•UweWoessner Universityof Stuttgart,HLRS, Germany

specializedequipmentavailableallowstocarryoutresearchbyusingAug-mentedReality,atechniquethatallowstooverlaytherealenvironmentwithcontextdependentcomputergeneratedimages.Researchersalsohaveaccesstothelatestdevelopmentsindisplaytechnology,likeholographicdisplaysortheabovementioned8kdisplays.

Theprojectmaintainsanalreadyhugedatabaseofvisualizationsoftwareandmodelsthatisavailableforallresearch-ersforfree.Thus,expertscanexplorethemultitudeofvisualizationpackagesthatarealreadyavailable.Softwarecoveredhererangesfromprocessingfilters,convertersandreaderstofullyfledgedmodellersandvisualizationpackages.VISIONAIRisroundedupbyseveralresearchactivitiesconcerningtheusabilityandaccessibilityofthefacilitiesandtheirsoftwareforexternalresearcherswithastrongfocusoncollaboration.

VISIONAIRisacommoninfrastructurethatgrantsresearchersaccesstohighlevelvisualisationfacilitiesandresources.Bothphysicalaccessandvirtualserviceswillbeprovidedbytheinfrastructure.Fullaccesstovisualization-dedicatedsoftwareisorganized,whilephysicalaccesstohighlevelplatformsisaccessibletootherscientists,freeofcharge,basedonthequalityoftheprojectsubmitted.Indeed,researchersfromEuropeandaroundtheworldarewelcometocarryouttheirresearchprojectsusingthevisualizationfacilitiesprovidedbytheinfrastructure.BycreatingthisEuropeanvisualizationnetworkitwillbepossibletocreatealandmarkthatwillhaveabroadvisibilitythroughoutthere-searchcommunitiesaroundtheworld.

Withinthisproject,theHLRSisprovidingaccesstoitsCAVE,Power-Walls,headmounteddisplaysanditshapticdrivingsimulator.Visitorswillbeabletointeractivelyvisualizelargesimulationresultsorevenrealizecomputationalsteeringandinteractivesimulationsbyleveragingthepowerofa40nodesvisualizationcluster.ThevisualizationsoftwareCOVISEwillnotonlyallowvisitorstoanalysetheirdatainVirtualRealitybuttheycanalsooverlaytheirvisualizationoverphysicalprototypesortestbedsusingAugmentedRealitytechniques.Thecollaborativefeatureswillallowthemtoanalysethesimulationstogetherwiththeircolleaguesathomeorwithremotescientists.

Projectcallsareexpectedtoopenendof2011.Interestedresearchersareinvitedtosubmitaproposalat www.infra-visionair.eu

PartnersINPGGrenoble(F),GrenobleINP(F),UniversityofPatras(GR),CranfieldUniversity(UK),UniversiteitTwente(NL),UniversitätStuttgart(D),PSNCPosnan(PL),UniversitédelaMediterranéMarseille(F),CNRGenova(I),INRIARennes(F),KTHStockholm(S),TechnionHaifa(IL),RWTHAachen(D),PoznanUniversityofTechnology(PL),ENSAMAix-en-Provence(F),TUKaiserslautern(D),UniversityofSalford(UK),FraunhoferIPKBerlin(D),i2catBarcelona(ES),UniversityofEssexColchester(UK),MTASZTAKIBudapest(HU),ECNNantes(F),UniversityCollegeLondon(UK),PolitecnicodiMilano(I),EMIRACLEBrussels(B).

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Forthepastthirtyyears,theneedforevergreatersupercomputerperformancehasdriventhedevelopmentofmanycomputingtechnologieswhichhavesubsequentlybeenexploitedinthemassmarket.Deliveringanexaflop(or10^18calculationspersecond)bytheendofthisdecadeisthechallengethatthesupercomputingcommunityworld-widehassetitself.TheCollaborativeResearchintoExascaleSystemware,ToolsandApplicationsproject(CRESTA)bringstogetherfourofEurope’sleadingsupercomputingcentres,withoneoftheworld’smajorequipmentvendors,twoofEurope’sleadingprogrammingtoolsprovidersandsixapplicationandproblemownerstoexplorehowtheexaflopchallengecanbemet.CRESTAfocusesontheuseofsixapplicationswithexascalepotentialandusesthemasco-designvehiclestodevelop:thedevelopmentenvironment,algorithmsandlibraries,usertools,andtheun-derpinningandcross-cuttingtechnolo-giesrequiredtosupporttheexecutionofapplicationsattheexascale.TheapplicationsrepresentedinCRESTAhavebeenchosenasarepresentativesamplefromacrossthesupercom-putingdomainincluding:biomolecularsystems,fusionenergy,thevirtualphysiologicalhuman,numericalweatherpredictionandengineering.

Nooneorganization,betheyahard-wareorsoftwarevendororserviceprovidercandeliverthenecessaryrangeoftechnologicalinnovations

requiredtoenablecomputingattheexascale.Thisisrecognizedthroughtheon-goingworkoftheInternationalExascaleSoftwareProject[1]and,inEurope,theEuropeanExascaleSoft-wareInitiative[2].CRESTAwillactivelyengagewithEuropeanandInternationalcollaborativeactivitiestoensurethatEuropeplaysitsfullroleworldwide.Overits36monthdurationtheprojectwilldeliverkey,exploitabletechnologiesthatwillallowtheco-designapplicationstosuccessfullyexecuteonmulti-petaflopsystemsinpreparationforthefirstexascalesystemstowardstheendofthisdecade.

OverallConceptandObjec-tivesoftheCRESTAProjectHPCsystemsexisttodeliverresultstotheirusersfromnumericalsimulationandmodellingapplications.AtthecentreofCRESTAarethereforesixapplicationsdesignedtoexecutewellonpetascalesystemstodayandthatwillbeexpectedtoexecutewellonexascalesystemstomorrow.

Co-designbyApplicationsEachoftheseapplicationshasbeencarefullychosen(a)asanapplicationthatcanbereasonablyexpectedtoneedtorunattheexascale(forrea-sonsofproblemsize,timetocomputeetc.)and(b)torepresentakeyusercommunitywithagrandchallengewhohavetheneedtocomputeattheexascaleinordertodelivertheirscientificorengineeringresults.

Collaborative Research into Exascale Systemware, Tools and Applications (CRESTA)

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Table1:CRESTA’sco-designapplications

Byunderstandingthecurrentstateofthelimitationsofthealgorithmsandproblemsizesforeachoftheseapplica-tions,CRESTAwillbeabletodevelopimprovedapplicationperformanceatthepetascaleonthencurrentsystems(perhaps100petaflop/sby2014)anddefineaclearroadmapforeachappli-cationtogetittotheexascalebytheendofthisdecademappedagainsttheexpectedhardwaredesignsweexpecttoseebytheendofthedecade.

SystemwareforExascaleHowever,applicationoptimizationandalgorithmicmodificationsonlyrepre-sentpartoftheexascalechallenge.

Systemsofthescaleenvisagedpresentenormouschallengesintermsofpowerrequirements,operatingsys-temissuessuchasresiliencyandprocessmanagement,communicationandprogramminglibraries,languages,compilers,debuggersandprofilers.Applicationsmustinteractwithmanyoftheseaspectsoftheexascalesys-temwarerequiredtocompile,link,run,debugandprofileapplicationcodes.

CRESTAwillthereforeusetheknow-ledgeavailablefromourtargetCrayplatform,withestimatesofwhatanexascalesystembuiltusingthesehardwaretechnologieswilllooklike

Application nameScientific grand

challenge domainPartner responsible

GROMACS Biomolecular systemsKUNGLIGA TEKNISKA HOEGSKOLAN (KTH)

ELMFIRE Fusion energyABO AKADEMI UNIVERSITY (ABO)

HemeLBVirtual Physiological Human

UNIVERSITY COLLEGE LONDON (UCL)

IFSNumerical Weather Prediction

EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS (ECMWF)

OpenFOAM Engineering

THE UNIVERSITY OF EDINBURGH, UNIVERSITY OF STUTTGART (HLRS, IHS), CENTRALE RECHERCHE SA (CRSA)

Nek5000 EngineeringKUNGLIGA TEKNISKA HOEGSKOLAN (KTH)

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Table2:Roleofapplicationsfordifferentresearchchallenges

fromasystemsperspective,andtherequirementsandformofeachofourapplicationscodes,tobuildandexploreappropriatesystemware.Inadditiontooperatingsystemfeatureswewilllookatcompilerandlibraryissues,howtodebugattheexascale(withAllinea’sDDTdebugger),howtooptimizeap-plicationperformanceattheexascale(withTUD’sVampirtool-suiteandKTH’sperfminer)andhowtomanagethepre-andpost-processingofdataresultingfromsimulations(anoftenneglectedareainsystemsdesign).ThebalanceoftechnicalworkinCRESTAwillbeapportioned40%onapplicationsand60%onsystemware.

Incrementalvs.disruptiveApproachesAkeyfeatureofCRESTAisitsuseofdualpathwaystoexascalesolutions.ManyproblemsinHPChardwareandsoftwarehavebeensolvedovertheyearsusinganincrementalapproach.

Mostoftoday’ssystemshavebeendevelopedincrementally,growinglargerandmorepowerfulwitheachproductrelease.However,weknowthatsomeissuesattheexascale,particularlyonthesoftwareside,willrequireacom-pletelynew,disruptiveapproach.Forexample,thecommunicationsoverheadofaparticularnumericalsolvermaygrowquicklywiththenumberofcores.Withmillioncoresystemsonthehorizon,theperformanceofsuchalgorithmsmaybesopoorthatallspeedupstops.Inthesecasesadifferentmethodofnumericalsolutionwillberequired.Thismaybehighlydisruptivetotheapplica-tioncodebutitwillsetitonthepathtoexecutingattheexascale.

CRESTAwillthereforeemployincre-mentalanddisruptiveapproachestotechnicalinnovation-sometimesfollowingbothpathsforaparticularproblemtocompareandcontrastthechallengesassociatedwitheachapproach.

GROMACS ELMFIRE HemeLB IFSOpen-FOAM

Nek5000

Underpinning and cross-cutting technologies

Significant Significant Significant Significant Significant Significant

Development environment

Significant Essential Essential Essential Significant Significant

Algorithms and libraries

Essential Useful Useful Essential Essential Essential

User tools (including pre and post processing)

Useful Significant Essential Useful Essential Useful

Application

ResearchChallenge

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•MarkParsons1

•StefanWesner2

1 EPCC, TheUniversity ofEdinburgh

2Universityof Stuttgart,HLRS, Germany

TheCo-designProcessAttheheartoftheproposalistheco-designprocess,involvingguidanceandfeedbackbetweentheco-designappli-cationsandthesystemwareworkpack-ages.Ahighlevelroadmaptoachievingexascaleforeachco-designvehiclewillbedevelopedaswillamoredetailedneedsanalysistoguidedevelopmentworkinthesystemwareWPs.Theyinturnwillinputexpertiseandprovidesolutionstothechallenges.Thiswillbeacyclicalprocess,theneedsanalysiswillbedynamicandupdatedregularlybasedonexperiencesofallthedevelop-ersacrossWPs.

Thesolutionstothechallengeswillbedifferentforeachapplicationandtheirintegrationwithdifferentworkpack-ageswilldependonthese.Foreachapplicationwehavecharacterizedtheirinteractionwitheachworkpackageonathree-pointscale.TasksareeitherEssential,SignificantorUsefulbasedonhowcriticalthedevelopmentistoenablingtheapplicationforexascale.

CollaborationandExploitationNooneorganization,betheyahard-wareorsoftwarevendororserviceprovidercandeliverthenecessaryrangeoftechnologicalinnovationsrequiredtoenablecomputingattheexascale.Thisisrecognizedthroughtheon-goingworkoftheInternationalExascaleSoftwareProjectand,inEurope,theEuropeanExascaleSoft-wareInitiative.Atthesametime,thePRACEResearchInfrastructure(PRACE-RI)[3]givesEurope,forthefirsttime,aleadership-classHPCresearchinfrastructureaccessiblebyanysuitablyqualifiedEuropeanscien-tistwithasuitableproblemtosolve.

Withinthisdecade,thePRACE-RIwillprovideexascalecomputingresourcesforEurope’sscientists.AsfouroftheleadingpartnersinthevariousPRACEprojects,thesupercomputingcentresrepresentedinCRESTAwillensureex-ploitationoftheresultsoftheCRESTAprojectbythePRACE-RI.

CRESTAwillcollaboratewithEESI[4]andanysubsequentprojecttoensurethatEurope’sexascaleresearchcom-munityhasanaturalmeetingplacetosharediscoveriesanddemonstrateleadershipontheworld-stage.TheCRESTApartnerswhoareinvolvedinIESPwillcontinuethisactivityandseek,whereappropriatetocollaborateontheInternationalstageasevidencedbythelettersofsupportincludedwiththisproposal.

AllnewsoftwaredevelopedbyCRESTAwillbemadeavailableasOpenSourceSoftware.

References[1] TheInternationalExascaleSoftware Project,http://www.exascale.org/ [LastaccessedAugust23,2011][2] TheEuropeanExascaleSoftwareInitiative, http://www.eesi-project.eu/ [LastaccessedAugust23,2011][3] PRACERI,http://www.prace-ri.eu/[4] EuropeanExascaleSoftwareInitiative, http://www.eesi-project.eu/

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IntroductionSupercomputersnowadaysconsistofhundredsofthousandsofprocessingunits.ForexampleHLRSwillinstalla1PFlopsCrayXE6withdual-socket16-coreAMDInterlagosnodesinover3,500nodes,i.e.112,000cores.Theparallelizationtechniquesofthepro-gramswhichrunonthesemachines,e.g.MPIandOpenMP,justtonametwoofthemostused,havehoweverbeendesignedwhenthelargestcom-puterswere100timessmaller.Duetoconstantdevelopment,thesetech-niquesstillwork,buttheyhavetobeexpandedbyadditionalprogrammingmodelsinordertokeepupthescalability.

ApromisingapproachistocombinethewidelyusedMPIwithsharedmem-oryparallelizationusingtaskbasedparallelizm,forinstanceSMPSs[1].Inthisprogrammingmodel,piecesofcode,e.g.functions,arespecifiedaspotentiallyparallelusingspecialmarkers.Giventhetasks'datadepen-dencies,eitherprovidedbythepro-grammerorextractedautomaticallybythecompiler,theprogram'stasksarescheduledtoworkerthreadsatrun-time.Programmingthreadsalreadyishard,dueitssharingofressourcesduetopossibleraceconditions-addtothatissuesofidentifyingandcorrectlystatingthetasks's

Debugging on the next Level: TEMANEjO

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Figure1:Asimpledependencygraph

withtentasksintwoindependentsub-

treesvisualizedbyTEMANEJO.Thenode's

interiorcolorrepresentthetask(the

taskifiedfunction),thesurrounding

margin'scolorrepresentthetask'sstate,

e.g.queued(yellow)orrunning(green).

Theredmarginsindicatethatthetask

hasunfulfilleddependenciesandcan

thereforenotbequeuedyet.Thenode

shapes(heretriangleandbox)denote

twodifferentworkerthreadsforrunning

tasks,whichmaybescheduledtodifferent

cores.Thetextlabelsandcoloursofthe

edgesindicatethememoryaddressesof

thedependencies.

dependenciesandperformanceimplicationsmakesitdifficultfortheprogrammertocomeupwithanefficientparallelizationstrategyofagivenprogram.

Wedevelopedagraphicaldebugger[2]whichiscapableofdisplayingtherelevantinformationinanaccessiblemannerandgivingtheprogrammermanypossibilitiesforinteractionwiththerunningprogram.

Thedifferencebetweentask-basedprogrammingmodelsandotherwaysofparallelisingapplicationsisthatitisnotknownaprioriwhenataskwillbeexecutedorwhichcorewillexecuteit.Theonlyinformationpertaskisthedatadependencies(basicallythedata'smemoryaddress),andthereforewhichothertasksdependonit.Asaresultadependencygraph,tobepreciseadirected-acyclicgraph(DAG)

iscreatedatruntime,withnodesbeingtasksandedgesbeingthede-pendenciesasseeninFigure1.

Newtaskswillbeaddedtothedepend-encygraphwhiletheprogramisrun-ningandfinishedtasksareremovedfromit.Whenforagiventaskallthedependenciesarefulfilleditcanbeexecutedbyanythreadatanytime.Thereforeeveryrunofanap-plicationcanresultinadifferentorderofexecutionondifferentpro-cessingunitsinthesystem.Thismakestaskbasedparallelprogramsextremelyhardtodebugwithnormaldebuggerssuchasgdb.Twoexamplesoftask-basedprogrammingmodelsareSMPSsandOMPSs.Botharede-velopedintheTEXTprojectwithparticipationoftheApplication,ModelsandToolsgroupattheHLRS[3].

AnewDebuggerTraditionaldebuggersworkcommandbasedonaperthreadbasis-toswitchbetweenconcurrentlyexecutingthreads,theprogrammerneedstoissuecommandsandretrievethestateofthethread.

Thatmeansonestepsthroughlinesofcodeandswitchesfromonethreadtoanother.Intaskparallelprogramming,onecannotseewhyataskcanorcannotbeexecuted.Moreover,uptonowtheprogrammerwaslefttoherowndevicesimaginingthedependencygraphorviewingpostprocessedgraphsfromprogramoutputs.

Wedevelopedagraphicaldebuggerfortaskparallelprogramsinorderenabletheprogrammertovisualizethedependencygraphandinteract

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withtheruntimewhiletheprogramisexecuting(seeFig.2).

Debuggingtheparallelcodecanbesepa-ratedintotwophases.Analysisofthedependencygraphandcontrolling,i.e.changingtheactualexecutionoftheapplication.Fortheformertheinfor-mationaboutthenodes(tasks)andedges(dependencies)ofthedependencygraphhavetobeextractedandcom-municatedtoavisualizationtool.Forthelatteracontroltool(whichcanbethesameasthevisualizationtool)willenabletheprogrammertosendcommands(inthefollowingcalledrequests)totheapplication.

Insteadofsteppingfromlinetolineorputtingbreakpointsatcertainlinesorfunctions,theprogrammerofatask-basedapplicationwantstostepthroughthedependencygraphandinhibitorforcetheexecutionoftasks.

Thisissupplementedbyattachingadebuggerlikegdbtotherunningprocessontheactualcomputenode.

Inordertoanalyzeandcontroltheexecutionofanapplicationweinstru-mentedtheruntimedeveloppedbyBarcelonaSupercomputingCenter(BSC)witheventhandlersatcertainpointsofthetaskslifecycle.Theeventhandlerisimplementedasaweakreferencetoalibraryfunctionwhichisdynami-callylinkedtotheapplicationusingtheLD_PRELOADenvironmentvariable.

ThiseventhandlerisimplementedasalibrarycalledAYUDAME(Spanishforhelpme)andiscalledatdistinctmomentsduringruntime.Itperformsnumeroustasks,topasstheinfor-mationtoanexternaltool,likeTEMANEJOviaTCPconnectionorreacttotheeventitself.

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•SteffenBrinkmann•ChristophNiethammer•JoséGracia•RainerKeller Universityof Stuttgart,HLRS

Figure2:ScreenshotofTEMANEJOrunninga

sparsematrixinversionparallelizedwithSMPSs.

Onthefrontendsideanytoolmayattachviatheopenprotocol-thedebuggerTEMANEJO(SpanishforIhandleyou)performstwotasks:

1.ItvisualizesthedependencygraphoftheStarSsapplicationgivingtheuserthepossibilitytoverifythecorrectnessofthetaskinterdependenciesandtooptimizethedependencystructure.

2.ItcontrolstheStarSsruntimeen-vironmentbysendingrequeststothelibrary.Requestscanbetopausetheruntimeunderspecificconditions,toblocktasks,orsingle-andmulti-stepthroughthegraph,orattachingadebuggerlikegdb.

FordisplayingthedependencygraphTEMANEJOneedsatleasttwopiecesofinformation:Whichtasksexistandwhataretheirdependencies.Theformerisreceivedwhentheprogramframeworkcreatestaskswhilethelatterisgatheredwhenthedependenciestothepredecessorsofeachnewlycreatedtaskareanalyzed.Furtherinformationpassedisthestatusofeachtask,i.e.whethertheyarequeued,runningorfinished,thememoryaddressofade-pendencyvariableandatimestamp.

AllofthisinformationisstoredinadatastructureinTEMANEJOforfurtheranalysis(e.g.numberofdependenciesofatask,longestandshortestpathsthroughthegraph,executiondurationoftasksetc.).Usingthenodecolour,nodemargincolour,nodeshapeandedgecolourasconfigurableindicators,

theprogrammercanaccesstheneededinformationinaconvinientandintuitiveway,allowingreductionofinformation,e.g.color-codingtheexecutingthread,showingimbalance.

ConclusionsWithAYUDAMEascalableandexten-sibleframeworkfortoolsfortaskbasedprogrammingmodelshasbeendevelopped.IthasbeentestedwithSMPSsandOMPSs,butcanbeusedwithanytaskbasedparallelizationmodelaslongasitisinstrumentedwithcallstotheAYUDAMEeventhandler.TheTEMANEJOdebuggerallowsvisualisingthedependencygraph,steppingthroughthetaskgraph,blocktasksandprioritisetasks.

Offeringthisfunctionality,TEMANEJOempowerstheprogrammeroftaskparallelapplicationstodebugandopti-mizetheapplicationefficiently.

References[1] Marjanovic,V.,Labarta,J., Ayguade,E.,Valero,M. Overlappingcommunicationand computationbyusingahybridMPI/SPMSs approach.InICS'10:Proceedingsofthe 24thACMInternationalConferenceon Supercomputing,pages5-16,NewYork, NY,USA,June2-4,2010,ACM

[2] Brinkmann,S.,Gracia,J., Niethammer,C.,Keller,R. TEMANEJO-adebuggerfortask-based parallelprogrammingmodels. InProceedingsofParCo'11,2011, submittedforpublication

[3] TEXT-TowardsEXaflopapplicaTions (www.project-text.eu)

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Figure1:LarKC–ahighperformanceSemanticWebreasoningplatform

TheessenceofSemanticWebistheideathattheWebcanexploittech-niquesfrom,e.g.formalKnowledgeRepresentation,tomakeinformationavailableinamachine-processablefor-mat,sothatamoreintelligentusersupportcanbeachievedontheWeb[1].Suchmachine-understandabledataformats,forinstancetheResourceDescriptionFramework(RDF),enablenovelusesoftheWebsuchasseman-ticsearch,dataintegration,statisticalanalysisandothers.RecentadvantagesinSemanticWebhaveforcedWebapplicationstoscaleuptotherequire-mentsoftherapidlyincreasingamountofinterconnectedanddistributeddatasuchasobservedintheLinkedOpenDatarepositoryfordatalocatedacross

theWebortheLinkedLifeDatase-man-ticintegrationplatformforthebio-medicaldomain,butalsoine-Scienceande-Commerce(e.g.Ontoprise).

ThemassiveandtremendouslygrowingamountofdatarequireseffectiveexploitationandishenceofagreatchallengeforthemodernITplatformsandinfrastructures.Anotherbigchal-lengeforachievingtheefficiencyandweb-scalingofSemanticWebappli-cationsistheheterogeneousnatureofexploreddataontheWeb,whichresultsindatainconsistencies,incom-pleteness,butalsoredundanciesduetovaryingmethodologiesusedduringthedatagenerationandcollection.

GiventhelargeproblemsizesaddressedbySemanticWebandconsideringthecomplexityofsomedataexplorationalgorithmssuchasRandomIndexingdescribedbelow,itseemsnaturaltoexplorethebenefitsofportingSemanticWebapplicationsforrunningonHighPerformanceComputingarchitectures.

LargeKnowledgeColliderOneofthemajorpracticalattemptstobuildaSemanticWebenginecapa-bleofprocessingbillionsofstructureddata,i.e.,web-scaledata,isperformedintheEUFP7projectLarKC(www.larkc.eu).LarKC,whichstandsfortheLargeKnowledgeCollider,buildsanexperimentalplatformformassivedistributedincompletereasoning(seeFigure1),whichaimsatremovingthescalabilitybarriersrecognizedformostofthecurrentlyavailablereasoning

Towards High Performance Semantic Web – Experience of the LarKC Project

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Figure4:Comparisonoftime(a)andbandwidth(b)ofinter-nodecommunicationofdifferentMPIlibrariesforJava(ontheHLRSNECNehalemclusterwithEthernetandInfinibandinterconnects).

Figure2:ParallelizationinSemanticWebapplicationworklows

engines.ThisgoalisachievedbymeansofanumberoftheoriginaltechniquesadoptedbyLarKC,e.g.ahighlyinnovativereasoningapproachformergingtheretrievalprocessandthereasoningbymeansofselection,identification,ortransformation[2].Ontheotherhand,LarKCenablesnumerousnovelITinfrastructuresolu-tionstosupportthoseoptimizationtechniques,suchasHighPerformanceorGridComputinge-Infrastructures.Theoptimalresourceprovisioningisofespecialimportanceforensuringtheweb-scalepropertyofSemanticWebapplications.However,sincein-troducingaspeciale-InfrastructureforSemanticWeb,asdoneinLarKC,processingvastamountofdataisnotamajorbottleneckanymore.

Nevertheless,leveragingthoseresourcesrequiresnecessaryadaptationsinthetraditional(serial)applicationcodes,i.e.theirparallelization.Theparallelizationbecomesthusamajorchallengefor

thenext-generationSemanticWebapplicationsexecutedinacontextofe-Infrastructure.

ParallelizationStrategiesadoptedbyLarKCInsolvingthoseissuesrelatedtothelarge-scaleSemanticWebapplications,LarKCallowsareasoningapplicationtobebuiltontopofnumerouslightweightSemanticWebcomputationalblocks(plug-ins,seetheactuallistonLarKCMarketPlaceathttp://www.larkc.eu/plug-in-marketplace),usedforidentifi-cation,selection,transformation,andactualreasoning.Whencombinedinacommonworkflow,suchasoneshowninFigure2,theseplug-inscanbeefficientlyutilizedforsolvingproblemsofthealmostvirtualdimensionality.

Data Parallelism

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Figure4:HybridMPI+JavaThreadscommunicationpattern.

Tosupportthisfeature,thecompositionoftheplug-insinaworkflowenablesparallelexecutionoftheplug-insonthehighperformanceresources.IntermsofLarKCapplications,theparallelizationsuggeststheidentificationofthecon-currentregionsoftheapplicationdata-aswellasinstructionflow,withfurthermappingthemtotheindependentpro-cessorunitsofaparallelsystem.

Amongthemostwidelyutilizedandsus-tainableinSemanticWebparallelizationapproaches,suchasMultithreading,Map-Reduce,aswellastheMessage-PassingInterface(MPI),thelatter(MPI)isthemostpromisingoneintermsoftheimplementationeffortsneededforaserialapplicationaswellasintermsoftheprovidedscalability.TherehavebeenseveralinitiativesstrivingtoprovideHPCsupportforJava,whichisde-factoadefaultprogramminglanguageintheLarKCSemanticWebcommunity.OneofthemostsuccessfulMPIimplementa-tionsforJavahasprovedtobempiJava,

chosenforadoptioninLarKC(Figure3).ThempiJavaframeworkiscurrentlydevelopedandsupportedbyHLRS.

MPIRealizationofRandomIndexingRandomIndexingisanovelapproachforvectorspacemodelling[3].Thevec-torspacerepresentsthedistributionalprofileofthewordsinrelationtotheconsideredcontexts/documents.Themainmethodicalvalueofthisprofileisthatitenablescalculationofthese-manticsimilaritybetweenthewordsinscopeofthedocumentcollection(textcorpus),basedonthecosinesimilarityfunctionofthegivenwords’contextvectors(1).

wherefqisaco-occurrencefunctionbetweenthewordxfromthewordsetXandeachofthecontextscjЄC

m,misatotalnumberofthecontexts,nisatotalnumberofthewordsinallcontexts.

However,suchpopularRandomIndex-ingimplementationpackages,suchasAirhead[4],areincreasinglyineffectivewhencomplexlyaddressinglargedataamounts,e.g.ascollectedbyLinkedLifeData.LarKChasexaminedthedomaindecompositionbasedparallelimplemen-tationofRandomIndexing,asdepictedinFigure4.

WithregardtotheaforementionedAirheadlibrary[5],verypromisingperformancecharacteristicswereobtainedforbothpureMPIandhybridMPI-JavaThreadsimplementations(seeFigure5).ThedocumentsetbasedonaselectionoftheWikipediaarticles(1Mhighdensityentries,16GBdiskspace)wasusedforthisperformance

Problem Domain

MPI Process 2MPI Process 1

Compute Node 1 Compute Node 2

Thread Pool Thread Pool

∀x∈Xn, ∃ v=[ fq(x,cj= )] (1)

∀x∈Xn, ∃ v=[ fq(x,cj= )] (1)

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•AlexeyCheptsov•MatthiasAssel


benchmark.Detailedresultsfordifferentinputdocumentsizesaswellasclusterconfigurationsarereportedin[6].

OutlookRecentadvantagesintheSemanticWebrequiretheunderlying(Java)applicationstoscaleuptotherequire-mentsoftherapidlyincreasingamountofprocesseddata,suchasthosecom-ingfrommillionsofsensorsandmobiledevices,orTBofdatavolumescon-ductedduringscientificexperimentsusinglaboratoryequipment.IntroducingHPCinSemanticWebdomaincangreatlysupportthischallenge.

Traditionally,theSemanticWebandtheHighPerformanceComputingcom-munityhavebeensomewhatdisjoint.However,astheneedsandcapabilitiesofthesetwocommunitiescontinuetoconverge,itturnstobebeneficialforbothtoleveragetheirrespectivetech-nologies.Parallelrealizationofserialcodesisakeyenablerofhighperfor-mancearchitecturesandisthereforeagreatchallengeforthemajorityofSemanticWebapplications.LarKCaimsatsimplifyingdevelopmentofhighperformance,parallelizedapplications,andthusbridgingthegapbetweenSemanticWebandHPC.

References[1] Daconta,M.C.,Smith,K.T.,Obrst,L.J. TheSemanticWeb:aGuidetotheFuture ofXml,WebServices,andKnowledge Management.JohnWiley&Sons,Inc.,2003

[2] Fensel,D.,vanHarmelen,F. UnifyingReasoningandSearchtoWeb Scale.In:IEEEInternetComputing11(2), pp.96-95,2007

[3] Sahlgren,M. Anintroductiontorandomindexing, MethodsandApplicationsofSemantic IndexingWorkshopatthe7thInternational ConferenceonTerminologyandKnowledge EngineeringTKE2005,pp.1-9,2005

[4] Jurgens,D.,Stevens,K. TheS-SpacePackage,AnOpenSource PackageforWordSpaceModels. ProceedingsoftheACL2010System Demonstrations,pp.30-35,Association forComputationalLinguistic,2010

[5] Cheptsov,A.,Assel,M.,Koller,B., Gallizo,G. EnablingHighPerformanceComputing forJavaApplicationsusingtheMessage- PassingInterface,P.IványiandB.H.V. Topping(eds.),ProceedingsoftheSecond InternationalConferenceonparallel, distributed,gridandcloudcomputing forengineering,2011

[6] Assel,M.,Cheptsov,A.,Czink,B., Damljanovic,D.,Quesada.J. MPIRealizationofHighPerformance SearchforQueryingLargeRDFGraphs usingStatisticalSemantics.Proceedings ofthe1stWorkshoponHigh-Performance ComputingfortheSemanticWeb, collocatedwiththe8thExtendedSemantic WebConference(ESWC2011),toappear in2011

Figure5:PerformancecharacteristicsoftheparallelRandomIndexingrealization(a)andcomparisonofpureMPIvs.MPI+JavaThreadscommunicationperformance(b).

Total Execution

Similarity Search

Doc.space Loading

MPI Communication

Pure MPI

MPI + Java Threads

CPU NodesCPU Nodes

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Figure1:“PlugyourBusinessintoIT”

BusinessandITAlignmentusingaModel-basedPlug-inFrameworkThealignmentofInformationTechnologyandBusinessisstillahighlycomplexandhardtoautomateprocessandremainsthereforemainlydrivenandperformedbyhumans.Bynature,thebackgroundandknowledgeofthosehumanscandiffer,dependingontheirrolewithintheirorganization.Sincedifferentpartiesoftendon´tshareacommonknowledgespace,thewholesituationislikelytobecomecomplex.

plugITinGeneralTheplugITproject[1]isbasedontheobservationofthenecessitytoalignBusinessandIT[2]duetotherolechangeofITfromanenablertoanindustrialsector.plugITaimsatdevelopinganITSocketthatrealizesthevisionof“plugging”businessintoITinawaysimilartotheoneusedto

provideelectricityviaasockettoanydevicethatcanbepluggedin.ThischallengecanbetakenupbycapitalizingonsemantictechnologiesforITGovernance.InplugIT,theNextGenerationModellingFramework[3]isdevelopedwhichreliesonresearchadvancesyieldingthefollowingbenefits:

• Atighterinvolvementofdomain expertsismadepossibletoexpress formalandsemi-formalknowledge viatheuseofgraphicalmodelling languages

• Differentgraphicalmodelling languagesfordifferentviews anddifferentlevelsofformal expressivenesscanbeused

• Domainspecificnotationsfor semanticsareintroducedby mergingformalconceptsof semanticswithgraphicalnotations

plugIT – Plug Your Business into IT

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Figure2:InteractionoftheplugITITSocketandtheOPSsystem

AnHLRSUseCaseHLRSisoneofthreeusecasepart-nerswithinplugIT.ThedetailedusecaseofHLRScoversanOnlinePro-posalSubmissionprocess(OPS)inwhichprojectapplicantscansubmitaprojectrequesttoaccessHLRScom-putingresourcesandperformtheircomputationaltasks.

Basedontherequirementsoftheprojectapplicant,modeltranslationsareusedtofindthebestfittingoffer,representedasasetofrecommenda-tionsforServiceLevelAgreements(SLAs)[4].TheITSocketsupportsthewholeprocessofcreatingaproposal,analysingtheproposalparameterswithrespecttoexistingmodelsandfinallyrecommendingandgeneratingSLAs.Thisisenabledthankstotheuseofaso-calledsemantickernelwhichusesgraphicalmodelscom-binedwithsemanticinformation.

InthecurrentproductionversionoftheOPSprocess,HLRSusesawebformbasedapplicationenablingaprojectapplicanttomakerequests

forcomputingresourcesatHLRS.Theapplicantcanentervariouspiecesofinformationdescribinghis/hercomputingresourceneeds.Oncesubmitted,therequestisanalyzedbyaprojectapproverandapprovedor,incasemodificationsareneces-sary,sentbackforupdates.Sofar,allthishasbeendonewithoutanyautomatedsupportingprocesseandhasreliedheavilyontheknowledgeoftheprojectapprover.

NecessaryEnhancementstocoverfutureDevelopmentsNow,withtheadventofnewparadigmslikeCloudComputing,thisprocessneedstobeenhanced.Whilstuptonowmostoftheapplicantscanbeassumedtobespecialistswithintheirdomain,whichmakestheprocesssimpletomanage,theofferingofcomputingresourcesneedstobecomemoreintel-ligentinthefuture.Inthelongterm,weneedtoensurethatalsoapplicantswithonlymoderateknowledgeoftheunderlyingsysteminfrastructureneedtobeabletoapplyforresources.

IT Provider

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Figure3:HLRSinplugIT

Moreover,theresourcesthemselvesarealsogettingmoreandmorecom-plex.TheacquisitionofanewCraysupercomputeratHLRS[5]isjustonesteptowardsanewgenerationofhighlycomplexinfrastructures.Theroleoftheprojectapproverthusgetsmoreandmoredifficultandthebenefitofanysupportingtechnologybecomesobvious.

TheplugITEnhancementsBymeansoftheplugITITSocket,HLRShasconcentratedontheprovisioningofsupportfortheprojectapprovers.Inparallel,therealizationoftheneces-sarystepsforintroducingSLAsintotheOPSprocesshavebeenaddressed.

AsplugITfollowsamodel-basedap-proach[6][7][8],thefirstactionwithintheprojectwastocreateanumberofreferencemodelswhichwereintendedtoprovidethenecessaryfoundations

fortheprojectdevelopment.ThiswasdoneviaanonlinemodelrepositorycontaininggraphicalmodelsrelatedtotheITinfrastructureandITservicesofHLRS.ModellingwasperformedwiththeNextGenerationModellingFramework,oneofthedeliverablesoftheproject.AllthemodelsarelinkedtoeachotherandtheyrepresenttheknowledgeofHLRSprojectapprovers.

TheOPSprocessisexecutednowasfollows:AprojectapplicantsendsarequestforcomputingresourcestotheHLRSprojectapprover.Uponre-ceptionoftherequest,theapprovermakesuseoftheITSockettogetbackfromitrecommendationsonwhichSLAtooffer.Thisisrealizedthroughtheautomatedprocessingoftheproj-ectapplicant’srequestbyasemanticworkflowoftheITSocket.Therecom-mendationsareSLAoffersthatdefinethecategoryoftheSLAthatcouldbe

plugIT IT Socket

plugIT NGMF

HLRS Project Approver Project Applicant

HLRS Modeller

1. IT Infrastructure (Compute Resources + Configuration) Models

2. IT Service (SLAs + Criteria) Models

create models

make request for compute resources

offer specific to compute resources

OPS application enacts HLRS workflow

send models

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•AxelTenschert•PierreGilet•BastianKoller

UniversityofStuttgart,HLRS

proposedtotheprojectapplicant.Inthecurrentscenario,thecategoriesarebronze,silverorgold,basedonthequalityoftheoffer.Eachofferrelatestoadedicatedcomputingresource.Inaddition,therecommendationsarerankedandvisualizedinawaysimplefortheprojectapprovertounderstandwhichSLAofferrecommendationsarethebestfittingtheprojectapplicant’srequirements.TheHLRSprojectap-proverhasthepossibilitytoviewthegraphicalmodelsifhe/sherequiresmoreinformationregardingtheSLAofferrecommendations.

Thepossibilitytopluginthebusinessrequirements–theprojectapplicant’srequest–intotheITdomainimprovestheefficiencyandoverallperformanceoftheOPSsystemandallowsHLRStobroadenitscustomerbase.Thegraphicalmodellingapproachalsoshowsobviousadvantagesintermsofmaintenanceofinformationandknowledgetransfer.

FactsplugITisaprojectfundedbytheEuro-peanUnionwithinthe7thFrameworkprogram.Theconsortiumconsistsofeightprojectmembers.plugITstartedontheMarch1,2009andwillrununtiltheAugust31,2011.

ThePartners• BOCAssetManagementGmbH(AT)• TelespazioItalia(IT)• UniversityofVienna,Departmentof Knowledge&BusinessEngineering(AT)• FoundationforResearchand TechnologyHellas(GR)• FachhochschuleNordwestschweiz(CH)• CINECA(IT)• InnovationTechnologyGroupSA(PL)• UniversityofStuttgart,HLRS(GER)

Websitehttp://plug-it.org/

References[1] plugITwebsite,http://plug-it.org

[2] Woitsch,R.,Karagiannis,D., Plexousakis,D.,Hinkelmann,K. BusinessandITAlignment:TheIT-Socket. E&IElektrotechnikundInformations- technik126,pp.308-321,2009

[3] NextGenerationModellingFramework Portalofthe2ndplugITPrototype: http://83.65.190.84/plugIT/workbench/

[4] Koller,B. EnhancedSLAManagementintheHigh PerformanceComputingDomain,Ph.D. Dissertation,UniversityofStuttgart,2011

[5] CraywinsSupercomputerContract FromtheUniversityofStuttgartvalued atmorethan$60Million, http://investors.cray.com/phoenix.zhtml?c =98390&p=irol-newsArticle&ID=1486975 &highlight

[6] Woitsch,R.,Karagiannis,D., Plexousakis,D.,Hinkelmann,K. PlugyourBusinessintoIT:Businessand IT-AlignmentusingaModel-basedIT-Socket, eChallengese-2009Conference, (eChallenges09),Turkey,IOSPress,2009

[7] Bork,D.,Sinz,E.J. DesignofaSOMBusinessProcess ModellingToolbasedontheADOxx Meta-modellingPlatform.IndeLara, J.,Varro,D.,Margaria,T.,Padberg,J., Taentzer,G.,eds.:4thInternational WorkshoponGraph-basedTools (GraBaTs2010),Enschede,The Netherlands,pp.89-101,2010

[8] Bézivin,J. ModelDrivenEngineering: AnEmergingTechnicalSpace, GenerativeandTransformational TechniquesinSoftwareEngineering, Volume4143,pp.36-64,2006

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Figure1:Sampleofsimulationinthedesignphase

EnergyconsumptionandimplicitCO2emissionsofcomputinganddatacen-treshaveincreaseddrasticallyoverrecentyearsandareexpectedtoin-creaseevenfurther.BesidetheraisingcostsforenergyconsumedbyITser-vicecentres,peoplearegettingmoreandmoreawareaboutthefollow-upofthehighdemandofenergyfortheITsector,liketheimpactonglobalwarm-ingandCO2emissions.Asanexample,

worldwidedatacentresCO2emissionsarealreadyequivalenttoabouthalfofthetotalairlines’CO2emissionsandareexpectedtoovercomethe40%ofTotalCostofOwnershipofworldwideITby2012.Datacentreelectricityconsumptionaccountsforalmost2%oftheworldproductionandtheirover-allcarbonemissionsaregreaterthanbothArgentinaandtheNetherlandstogether[1].

GAMES (Green Active Management of Energy in IT Service centres)

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Figure1:Sampleofsimulationinthedesignphase

Sincecomputingdemandandelectricitypricesarerisingwhilstbecomingdwin-dlingresources,energyconsumptionofITsystemsanddatacentreenergyefficiencyareexpectedtobecomeapriorityfortheindustry.Despitethefactthatmanystakeholdershavebeenundertakingsignificanteffortsinde-liveringnewyetmoreenergyeffectiveITequipmentallowingsignificantcostandenergysavings,unfortunatelytheproblemoftheenergyefficiencyofInformationSystemsasawholehasnotbeenproperlyaddressedsofar.

GreenComputingisanewdisciplineandpracticeaimedatdesigningandusingcomputingresourcesinanenviron-mentally-awareway.Itwasoriginatedmorethanadecadeagowiththemaingoalofreducingenergyconsumptionofcomputingresources,yetmaintainingaclearfocusontheimpactontheen-vironment.AlthoughmanyprogresseshavebeenmadebyGreenComputing,makingnewchipsandserversavail-ablewhichundoubtedlyconsumelessenergy,inmostcasesimprovementsinefficiencyaredevouredbyincreasingdemandforcomputingpowerandca-pacity,drivenbynewdigitizedbusinessprocessesandservices.

TheGAMESproject[2]aimsatdevelop-ingtoolsandmethodologiestoimprovetheenergyefficiencyofITservicecen-tresbyenablingactivemanagementofresourcesandsoftwareequally.Whilststoragehostscanprincipallyreducethecomputingfrequencytosaveenergy,computeprovidersandinparticularHighPerformanceComputingcentreshavemoredynamicandchangingdemandstowardstheinfrastructureusage–suchastheflexibledegreeofscaleoutofaprocessorthedifferentscopeofdataaccessandstorageof

differentapplications.Mostresourcesinsuchanenvironmentdonotallowforfastenoughadaptationoftheirenergyparameterswithoutaffectingtheover-allperformance.Whatismore,mostparametersandrelationshipsbetweenusageandconsumptionarenotevenknownasyet,e.g.thetotalenergyprofileofanapplicationthatrunsatmaximumCPUclockrateforashorttimemaybelowerthanthatofthesameapplicationrunningathalfclockrateforalongertime,dependingonthebehaviouralprofile.

ThereforetheGAMESprojectwillex-aminetheenergyprofilesofdifferentapplicationsandsystemsaccordingtotheirspecificbehaviourinmoredetail,derivingenergyprofilesfromthiswhichindicatehowtoconfiguretheinfrastructureforbestenergyandperformanceefficiency.Itwillexposetheprofileparameterstoenablede-veloperstowriteenergyefficientap-plicationsandconfigureperformanceaccordingtotheirneeds.GAMESwillfurthermoredevelopamonitoringandmanagementsystemtightlycoupledtotheresourceinfrastructure,thusenablingdynamic,flexibleandimmedi-atereactiontochangingrequirements,withoutaffectingtheoverallexecutionperformance.

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Inparticular,theGAMESprojectaimsatdevelopingasetofinnovativemethodologies,metrics,OpenSourceICTservicesandtoolsfortheactivemanagementofenergyefficiencyofITServiceCentres.Itfocusesonthefollowingtwoaspects:

1. Co-designofenergy-aware informationsystemsandtheir underlyingservicesandITservice centrearchitecturesinorderto satisfyusersrequirements(Quality ofService),serviceperformance, context,addressingenergyefficiency andcontrollingemissions(cp.Fig.1). AcombinationofGreenPerformance Indicatorsareproposedtoevaluateif andtowhatextentagivenservice andworkloadconfigurationaffects thecarbonfootprintemissions’levels;

2.Run-timemanagementofIT ServiceCentreenergyefficiency, exploitingtheadaptivebehaviourof thesystematruntime,bothatthe service/applicationandITarchitec- turelevels(includingITcomponents likeservers,andstorage),whilst consideringtheinteractionswiththe facilitymanagementaswellinanover- allunifyingvision.

Inparticular,GAMESwilladvancethecurrentscientificandtechnologystate-of-the-artinenergyefficiencyforITser-vicecentresinthefollowingdomains:

• GAMESwillcreateandmake availableanintegratedmethodology (GAMESco-designmethodology) fortheshareddesignof“GreenIT ServiceCentres”,trading-offQuality

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•AlexanderKipp


ofServices,users’businessand functionalrequirementsagainst energyefficiencyandemissions;

• GAMESwillcomplementandextend oneofthemostleading-edgeOpen Sourcedatacentremonitoring tools,namelyNAGIOS,withthe capacityofassessing,monitoring andcontrolling,bothinareactive andproactiveway,energycosts andemissions(GAMESEnergy EfficiencyTool)inrealtimeof alternativeyetviableoptions/ configurationsfordistributing servicesamongthevirtualized machines,workloadamongservers andstoragedevicesaswellas balancingpoweragainstheat/ temperatureatfacilitylevel;

• TheGAMESweb-toolwillbeenriched withadvancedknowledge-basedand informationextractionfeatures (GAMESKnowledge&MiningModule) byoriginallycombiningdatamining, semanticandcontextmanagement technologiesforcloselyaligning usersbusinessrequirementsfor powerdemandwithhistoricaltrend andrealavailableresources;

• TheGAMEStoolingframeworkwill provideanadaptivecontrolfeature (GAMESMonitoring&Adaptive ControlInfrastructure),matchingthe plannedbehaviourwiththeoutput dynamicallyprovidedbytheenergy sensingandmonitoringinfrastruc- ture,theusercontextandhistorical patterns,forevaluatingifandtowhat extenttheadoptedcourseofactions willcontributetoeffectivelymanage theenergyefficiency;

• GAMESwilldefinecomprehensive energyefficiencyassessment

metrics(GAMESGreenPerfor- manceIndicators)asanenabler tocombineenergyefficiencyfacility featureswithITinfrastructureand business/applicationenergyfeatures.

HLRSisparticipatingintworoleswithinGAMES.FirstofallHLRSisintheroleofapotentialend-userofGAMESasanationalsupercom-putingcentrewithincreasingpowerdemandsofcurrently5MWforoperatingthedifferenthardwaresystemsforacademiaandindustry.InthisroleHLRSalsosupportstheactivitiesincreatingtheknowledgeandinformationbasefortheman-agementframework.AdditionallyHLRScontributestoGAMESasatechnologyandsoftwareproviderofhighlyscalablemonitoringsolu-tionsandserviceorientedarchitecturedrivenITsolutionswithaHighPerfor-manceComputingfocus.

Participants• EngineeringIngegneriaInformatica,I• PolitecnicodiMilano,I• HighPerformanceComputing CenterStuttgart(HLRS),GER• TechnicalUniversityofCluj-Napoca, RO• IBMISRAEL- ScienceandTechnologyLTD,IL• ChristmannInformationstechnik,GER• ENERGOECO,RO• ENELSi,I

References[1] Kaplan,J.M.,Forrest,W.andKindler,N.RevolutionizingDataCenterEnergyEfficiency.McKinsey&Company,July2008

[2] http://www.green-datacenters.eu

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Figure1:PartofexemplaryschedulinggraphofSMPSswithcommunicatingtasks

Withmany-coreprocessorsofferingevermorecomputepowerpersocket,andlarge-scalesupercomputersbuiltfromthesebricks,thelong-lastingdiscussionsonparallelprogrammingmodelsarebeingposedagain.VenturingintotherealmofPetascaleapplications,severalkeyquestionsregardingscal-abilityintermsofmemoryandprocess-ingoverheadperparallelinstantiationareconsideredandweighedagainsttheneedforportability,readabilityandmaintainability.

TheTEXTprojectisfundedbytheECaspartoftheINFRA-2010callfortwoyears.TheninepartnersfromSpain,GermanytheUK,France,GreeceandSwitzerlandsharethevision,thatthekeycomponenttosupporthighproduc-tivityandefficientuseofasystemistheprogrammingmodel.Amongthepart-nersarefourHPCcenters,alsomem-bersofthePRACEcollaboration,withJSChavingaPetaflopsmachineinpro-duction.TheprojectcentersaroundtheMPI/SMPSs,whichispartoftheStar-SuperScalar(StarSs)modeldevelopedbyBarcelonaSupercomputingCenter.

OverviewTheTEXTproject’stechnologycom-binestheavailablescalabilityoftheMessagePassingInterface(MPI)acrosscomputenodeswiththepos-sibilitiesofper-nodeconcurrencyviaasynchronoustaskofSMP-Superscalar(SMPSs).GivenanexistingapplicationsusingMPIforworkdecomposition,theprogrammermayfurtherparallelizetheapplicationintoso-calledtasksusingSMPSs.Thesetasksthenaredynami-callyscheduledbytheSMPSsruntimeenvironmenttobeexecutedasynchro-niously.Theruntimegeneratesagraphandefficientlymapsready-to-executetasksontotheavailablecores,takingcareofdependenciesamongthetasks.WithlessMPIranksrunning,onehaslowerconnectivity,thereforelowermemoryoverheadforMPI-internalbuf-fers,andpotentiallybiggermessagesizes,whichtogetherwiththeSmpSstaskmodelallowforbettercommu-nication-computationoverlap.Inthelattercase,theMPIcommunicationishandledwithinanSMPSs-taskandscheduledbytheruntime,wheneverthecomputeddataisavailableatthesender.

SimilartoOpenMP,theprogrammerannotatesherapplicationusingprag-mas,specifyingthefunctionstoberunastask,theirinput,outputandinoutparameters,aswellastheirsizes.Anexampleofasimplefunctionmaybe:

#pragma css task input(SIZE)

inout(v[SIZE])

void compute_vector (float *v,

int SIZE){...}

Towards EXascale ApplicatTions (TEXT)

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•RainerKeller•JoséGracia


Aftertheprogrammerhasinitializedtheenvironmentusing#pragmacssstart,anycalltotheabovefunctionwillbeasignedtoathreadandexecutedasynchronouslyonthecoresofthenode.Furthermore,synchronizationpointsmaybenecessarytowaitforandguarantueeacommonviewonthecomputeddata.Themainpointhoweverhereissimplepoint-to-pointdependencybetweenasynchronouslyexecutedtasks,whichallowthegraphschedulertomoreflexiblyparallelizeindependenttasks.

Usingapre-compiler,inourcaseSMPSs-cc,theannotatedCorFortransourcecodeisamendedwithfurtheradministrativecode,andfinallypassedtothenativeback-endcompiler.Usingthe-keepcompileroption,thepro-grammermayseetheactuallygener-atedintermediatesourcecode,whichiscompiledbytheback-endcompiler.

AimoftheProjectTheStarSsprogrammingmodelhasshowngoodresultsinitsGridSsandCellSsincarnationsforexecutionintheGridandontheIBMCellarchitecture,respectively.IntheTEXTproject,wehopetoextendtheprogrammingmodelontotheexistingMPI-parallelapplications,whichareimportanttothecomputecenters.

TheseapplicationschosenhavebeenusedalreadyinthecontextofthePRACEproject,andincludeSPECFEM3d(UPPA),PSCandPEPC(JSC),BESTandLS1(HLRS)andCPMD(IBM).BasedontheMPI-parallelversion,combinationofthenode-localparallelizationusingSMPSsplusMPIwillbeinvestigatedusingperformanceanalysis.

Eachapplicationoffersitsownchal-lenges,e.g.LS1beingaC++codehasveryelaborateclassstructureandisoneofthefirstC++codestobeusedwithStarSs,whileBESTusessomeofthemoreintricatefeaturesofFortran95andFortran2003.BothofthesecodesarebeingportedtoMPI+SMPSs.

Whiletheoptiontokeeptheinter-mediatecodeallowsusingtraditionaltoolstowork,thisiscumbersome.Thereforeperformanceanddebug-gingtoolswillbeenhancedtosupportthespecialrequirementsofSMPSs.Forexample,beingabletodebugwith-outhavingtofallbacktodebuggingtheintermediatecode,orbeingabletodebugusingbreak-pointsintasksbeinggenerated.

Toenhanceperformance,itwillbenecessarytoevaluateproperchunksizesofthetasksandestimatetheover-headintroducedduetodependencies.

ThePartners• BarcelonaSupercomputingCenter (BSC)• HighPerformanceComputing CenterStuttgart(HLRS)• JülichSupercomputingCenter(JSC)• EdinburghParallelComputingCenter (EPCC)• FoundationforResearchand TechnologyHellas(FORTH)• UniversityofManchester(UMAN)• UniversitédePauetdesPaysde l’Adour(UPPA)• UniversitatJaumeIdeCastellón (UJI)• IBMResearch,Zurich

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Sarah Jones [email protected]

Norbert Kalthoff [email protected]

Rainer [email protected]

Manuel Keß[email protected]

Alexander [email protected]

Markus J. [email protected]

Bastian [email protected]

Thomas C. [email protected]

Zi Yang [email protected]

Claus-Dieter [email protected]

Alejandro [email protected]

Christoph [email protected]

Mark [email protected]

Authors

Christoph Altmann [email protected]

Fakher F. [email protected]

Matthias Assel [email protected]

Felix [email protected]

Arne [email protected]

Steffen [email protected]

Alexey Cheptsov [email protected]

Tillmann A. [email protected]

Volker Gaibler [email protected]

Leonhard Gantner [email protected]

Gregor [email protected]

Pierre [email protected]

José [email protected]

Michael [email protected]

Ulrich Rist [email protected]

Juliane Schwendike [email protected]

Björn Selent [email protected]

Marc [email protected]

Axel [email protected]

Stefan [email protected]

Stefan [email protected]

Uwe Wössner

[email protected]

PublisherProf.Dr.-Ing.Dr.h.c.Dr.h.c.MichaelM.Resch

Editor&DesignF.RainerKlank,HLRS [email protected]öhlig,HLRS [email protected]äusser,HLRS [email protected]

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ArticlesarereprintsofinSiDEVol.8No.1Spring2010-Vol.9No.2Autumn2011

inSiDEispublishedtwotimesayearbyTheGaussCentreforSupercomputing(HLRS,LRZ,JSC)

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GCS HLRS

High Performance Computing Center StuttgartNobelstrasse 19 | 70550 Stuttgart | Germanyphone ++49 (0)7 11 - 685 - 8 72 69fax ++49 (0)7 11 - 685 - 8 72 09www.hlrs.de

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