UNISDR Global Assessment Report - Current and Emerging ... · Leibniz Supercomputing Centre (LRZ)...
Transcript of UNISDR Global Assessment Report - Current and Emerging ... · Leibniz Supercomputing Centre (LRZ)...
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UNISDRGlobalAssessmentReport-CurrentandEmergingDataandComputeChallengesMattiHeikkurinen*,DieterKranzlmüller
MunichNetworkManagementTeamLudwig-Maximilians-UniversitätMünchen(LMU)&LeibnizSupercomputingCentre(LRZ)oftheBavarianAcademyofSciencesandHumanities
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Contents
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Environmental computing casestudy
1. Leibniz Supercomputing Centre,MNM-Team,UNISDR
2. What isenvironmental computing?
3. UNISDRcollaboration indetail
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LeibnizSupercomputingCentreoftheBavarianAcademyofSciencesandHumanities
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Withapprox.230employeesformorethan100.000studentsandformorethan30.000employeesincluding8.500scientists
• EuropeanSupercomputingCentre• National SupercomputingCentre
• RegionalComputerCentreforallBavarianUniversities• ComputerCentreforallMunichUniversities
Photo:ErnstGraf
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SuperMUC Phase 1 + 2
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Top500SupercomputerList(June2012)
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www.top500.org
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SuperMUCSystem@LRZ
Phase2(LenovoNeXtScale WCT):
• 3.6PFlops peakperformance• 3072LenovoNeXtScale nx360M5WCT
nodesin6computenodeislands• 2IntelXeonE5-2697v3processorsand64
GBofmemorypercomputenode• 86,016computecores• NetworkInfiniband FDR14(fattree)
CommonGPFSfilesystemswith10PBand5PBusablestoragesizerespectivelyCommonprogrammingenvironment
Directwarm-watercooledsystemtechnology
Phase1(IBMSystemxiDataPlex):
• 3.2PFlops peakperformance• 9216IBMiDataPlex dx360M4nodesin18
computenodeislands• 2IntelXeonE5-2680processorsand32
GBofmemorypercomputenode• 147,456computecores• NetworkInfiniband FDR10(fattree)
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LRZ Application Mix
n Computational Fluid Dynamics: Optimisation of turbines andwings, noise reduction, air conditioning in trains
n Fusion: Plasma in a future fusion reactor (ITER)n Astrophysics: Origin and evolution of stars and galaxiesn Solid State Physics: Superconductivity, surface propertiesn Geophysics: Earth quake scenariosn Material Science: Semiconductorsn Chemistry: Catalytic reactionsn Medicine and Medical Engineering: Blood flow, aneurysms, air
conditioning of operating theatresn Biophysics: Properties of viruses, genome analysisn Climate research: Currents in oceans, hydrometeorology
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LinkwithComputerScienceandmanagement:MNM-Team
• MNM-Teamhistory• Established 25years ago,LMU,TUM,LRZ• One of the first groups to address ITmanagement• Peopleprocesses behind 80percent of mission critical ITservice downtime
• Researchinterests• Manageability of networked systems:concepts,tools,processes• From basic ITresearch to providing research ITservices (code to consulting)
• Ongoing activities• PiCS partnership:redefining the interface between computational scientist
and supercomputing• Environmentalcomputing:supporting interdisciplinary production of
„actionable knowledge“
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UNISDR– TheUnitedNationsOfficeforDisasterRiskReduction
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https://www.unisdr.org/
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GAR– GlobalAssessmentReportonDisasterRiskReduktion2015
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http://www.preventionweb.net/english/hyogo/gar/2015/en/home/GAR_2015/GAR_2015_6.html
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NumberofDisastersperRegion
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http://www.emdat.be/disaster_trends/index.html
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MunichRe– LossEventsWorldwide2014
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http://www.preventionweb.net/files/41773_munichreworldmapnaturalcatastrophes.pdf
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Whatisenvironmentalcomputing?
n …andwhy LRZ&MNM-Teamare interested?
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What if we could predict flash flood?
n Fromnowarningtoevenafewhour’swarning– Preventlossoflife– Reducematerialdamagesconsiderably
n Whatwasneeded?– Computingcapacity– Modelchains
n Cansofwormsopened:– Everymodelrequiresdifferentenvironment(OS,libraries,…)– Everymodeldescribestheserequirementsdifferently– Datastandardstendtobedifferent– …anddescribed inbespoke way– Optimal hardwarevaries
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CaseDRIHM
n Workflow linking rainfall,discharge andwater levelandflow
n ”Plugandplay”framework formodels anddata
n Casestudiesdemonstratingcapability forbetter advancewarnings
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Theoriginoftheidea– déjàvu
n Mostoftheinter- ortransdisciplinaryprojectsmergingtheirsolutionsintonewlarge-scaleservicesgothroughthesimilarprocessinunderstanding– Relationshipsbetweenthecomponents– RelationshipwiththecomponentsoftheITinfrastructure– Understandingwhoisthe“customer”– Consensusaboutthehigh-levelservicedescription– Semanticsofthe“glue”linkingcomponentstogether
n Theexactsolutionstendtobedifferent,butalreadysharedawarenessoftheissuehelps
n Communitybuildingasawaytocatalyse(eventual)standardisation
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Workingdefinition of environmentalcomputing?
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n Producingactionableknowledge relatedtoenvironmentalphenomenausingadvancedmodellingapproaches– e.g.multi-model,multi-scale,multi-data– Usingnon-trivialcomputingresources– Serviceorientation:reusablesolution,“robust”indifferentcontexts
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Whystarta„newdiscipline“
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n Havinganame– Bringspeopletogether
• Includingpeopleinfundingagenciesandindustry– Reducesacademiccareerriskofdoingsomethinginter-disciplinary
• “FringeDwellers” seenasessentialbypractitioners• Formalrules:underperformingornon-relevant
– Sparksthedevelopmentofcommonbodyofknowledge
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Success story based onsimilar transdisciplinaryinitiative
n Historyofmedicalinformatics– Rootsinthe1950’s- ComputerisedEKGanalysis,electronicpatientrecords
– ACMSIGinBiomedicalComputing1960– Nameofthefielddiscussedthroughoutthe70’s
n Structures– Firstassociationsin80’s– Curricularecommendationsin90’s
n Situationtoday:– Marketsizeestimatesbetween6,5and12,5b$(2012,2015)
– Thousandsofregisteredmembersinprofessionalassociations
– RecognisedspecialtyinrecruitmentISESS- 10May2017 M.Heikkurinen,D.Kranzlmüller 22
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Environmentalcomputing conclusions
n Supportingadvancedenvironmentalmodellingrequiresnewapproaches– Tomanageboth theinterfacetoITservicesandbetweendifferentspecialisations developingmodels
n Networkinginitiativeratherthanformaldefinition– Weexpectdefinitiontoemergegraduallythroughsharedexperiencesandcross-pollination
n DevelopmentoftheITplatformsbringopportunitiesandchallenges– NewITarchitecturesrequireadaptationofsoftware– AdaptLRZapproachestoextremescaling(workshops,PiCSparnership model)
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n TheUNISDRdataandcomputechallenge
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UNISDRGARprocess
Hazard 1 modelling
team
Hazard 1 modelling
team
Hazard N modelling
team
Hazard 1 modelling
team
Exposure modelling
team
Vulnerability modelling
team
Risk computation
team
GAR analysis
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HistoryoftheUNISDRcollaboration
n Informaldiscussionsin2014,contactthroughDRIHMprojectn Discussionre,speedingupthelosscalculation
– “Chinacalculationtakes5weeks,canyoulendusasupercomputer”n Informalcollaboration,calculationtimetofewdays
– Firstparallelversionintooperationaluse
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Hazard 1 modelling
team
Hazard 1 modelling
team
Hazard N modelling
team
Hazard 1 modelling
team
Exposure modelling
team
Vulnerability modelling
team
Risk computation
team
GAR analysis
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Nextchallenge:openGARdata
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Hazard 1 modelling
team
Hazard 1 modelling
team
Hazard N modelling
team
Hazard 1 modelling
team
Exposure modelling
team
Vulnerability modelling
team
Risk computation
team
GAR analysis
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GARopendataproduct
n Existingdemandfromthecommunity– Currentlysharedonrequest– Opendatatolowerthethresholdofreuse– Mainissue:sufficientinfrastructure
n CurrentproductionprocessdrivenbytheGARcycle– Newdocumenteverytwoyears– Simpleversioning,implicitmetadata
n Futurechallenges– On-demandprocess,multipleversions?– Morediverseuses,moreopportunitiesformisunderstandings
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GARopendatasolutionhistory
n Version0– SimplymountthedatadiskonawebservermanagedbyMNM-Team
n Version1– ”Shell”aroundthedirectoryhierarchy– Newlookandfeel– Additionalfunctionality(directoryandfiledescriptions,downloaddirectory
contents)n Version2
– Basedonadvancedresearchdatamanagementsystems– Automaticgenerationofmetadata,workflows,versioning,…– Mainissue:nosupport forcurrent implicit metadata(directory path)
n Version3– Refinement ofversion1(!)
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GARdataportal- beta
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Nextsteps
n Observetheuseofdataportal– Whowilluseit?– How?– Arethenewusecasesinadditiontoexpectedones?
n AdapttothenewGARprocess– TobedeterminedinCancunGlobalPlatformevent
n ComputeanddatasolutionforthewholeGARlifecycleprocess?
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Hazard 1 modelling
team
Hazard 1 modelling
team
Hazard N modelling
team
Hazard 1 modelling
team
Exposure modelling
team
Vulnerability modelling
team
Risk computation
team
GAR analysis
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n Conclusions
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Observations related to LRZand MNM-Teamrole
n Weunderstandthepotentialimpactofadvancedenvironmentalmodelling– Andhaveamandatetopursueresearchinthedomain
n ThegapbetweentheBigData/Supercomputingpracticesandtherealityofmostofthepractitioners– Stateoftheartsolutionsmayhavehighup-frontorganisational ortechnicalinvestments
– Needtohavetheinitialsuccesstogivetimeforreflection!n LRZandMNM-Teamarepromotingenvironmental
computingasamethodtofillthisgap– ”Branding”tomakeinterestvisible
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Dataandcomputechallenge
n BetweeneverydaydataandBigDataexiststhe“Awkwarddata”domain– TBrange– Typicallycollaborativeeffortswithemergingstandardsandpracticesetc).– UNISDRaperfectexample
n Computingchallengesimilar– Theroadfromasinglethreadedorsharedmemoryparallelismtoefficient
cluster/supercomputingapproachesischallenging– Effort/returnratioisveryunlinear!
n Thereisprobablynosingletechnicalsolutiontothischallenge,butorganisation/processthekey– “PiCS approachtoenvironmentalcomputing”– Workshops– Networking
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Upcoming events,contact
n UNISDRGlobalPlatform– 22-26May,Cancun,Mexico– http://www.unisdr.org/conferences/2017/globalplatform
n Upcomingenvironmentalcomputingworkshops– ICCS,June14th 2017(http://www.envcomp.eu/ICCS17)– Enviroinfo 2017,Luxemburg13-15September
• Workshop"AppliedEnvironmentalModelling– OperationandImpact”(http://www.enviroinfo2017.org/)
– eScience 2017,24-27October,Aucland,NewZealand(http://escience2017.org.nz/)
n Contactandmoreinformation– [email protected]– Envcomp website:www.envcomp.eu
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