UNISDR Global Assessment Report - Current and Emerging ... · Leibniz Supercomputing Centre (LRZ) ... SuperMUC Phase 1 + 2 ISESS -10 May 2017 M. Heikkurinen, D. Kranzlmüller 4. Top

UNISDRGlobalAssessmentReport-CurrentandEmergingDataandComputeChallengesMattiHeikkurinen*,DieterKranzlmüller

MunichNetworkManagementTeamLudwig-Maximilians-UniversitätMünchen(LMU)&LeibnizSupercomputingCentre(LRZ)oftheBavarianAcademyofSciencesandHumanities

Contents

ISESS- 10May2017 M.Heikkurinen,D.Kranzlmüller 2

Environmental computing casestudy

1. Leibniz Supercomputing Centre,MNM-Team,UNISDR

2. What isenvironmental computing?

3. UNISDRcollaboration indetail

LeibnizSupercomputingCentreoftheBavarianAcademyofSciencesandHumanities


Withapprox.230employeesformorethan100.000studentsandformorethan30.000employeesincluding8.500scientists

• EuropeanSupercomputingCentre• National SupercomputingCentre

• RegionalComputerCentreforallBavarianUniversities• ComputerCentreforallMunichUniversities

Photo:ErnstGraf

SuperMUC Phase 1 + 2


Top500SupercomputerList(June2012)


www.top500.org

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)


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


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“


UNISDR– TheUnitedNationsOfficeforDisasterRiskReduction


https://www.unisdr.org/

GAR– GlobalAssessmentReportonDisasterRiskReduktion2015


http://www.preventionweb.net/english/hyogo/gar/2015/en/home/GAR_2015/GAR_2015_6.html

NumberofDisastersperRegion


http://www.emdat.be/disaster_trends/index.html

MunichRe– LossEventsWorldwide2014


http://www.preventionweb.net/files/41773_munichreworldmapnaturalcatastrophes.pdf

Whatisenvironmentalcomputing?

n …andwhy LRZ&MNM-Teamare interested?


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


CaseDRIHM

n Workflow linking rainfall,discharge andwater levelandflow

n ”Plugandplay”framework formodels anddata

n Casestudiesdemonstratingcapability forbetter advancewarnings


Theoriginoftheidea– déjàvu

n Mostoftheinter- ortransdisciplinaryprojectsmergingtheirsolutionsintonewlarge-scaleservicesgothroughthesimilarprocessinunderstanding– Relationshipsbetweenthecomponents– RelationshipwiththecomponentsoftheITinfrastructure– Understandingwhoisthe“customer”– Consensusaboutthehigh-levelservicedescription– Semanticsofthe“glue”linkingcomponentstogether

n Theexactsolutionstendtobedifferent,butalreadysharedawarenessoftheissuehelps

n Communitybuildingasawaytocatalyse(eventual)standardisation


Workingdefinition of environmentalcomputing?


n Producingactionableknowledge relatedtoenvironmentalphenomenausingadvancedmodellingapproaches– e.g.multi-model,multi-scale,multi-data– Usingnon-trivialcomputingresources– Serviceorientation:reusablesolution,“robust”indifferentcontexts

Whystarta„newdiscipline“


n Havinganame– Bringspeopletogether

• Includingpeopleinfundingagenciesandindustry– Reducesacademiccareerriskofdoingsomethinginter-disciplinary

• “FringeDwellers” seenasessentialbypractitioners• Formalrules:underperformingornon-relevant

– Sparksthedevelopmentofcommonbodyofknowledge

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

Environmentalcomputing conclusions

n Supportingadvancedenvironmentalmodellingrequiresnewapproaches– Tomanageboth theinterfacetoITservicesandbetweendifferentspecialisations developingmodels

n Networkinginitiativeratherthanformaldefinition– Weexpectdefinitiontoemergegraduallythroughsharedexperiencesandcross-pollination

n DevelopmentoftheITplatformsbringopportunitiesandchallenges– NewITarchitecturesrequireadaptationofsoftware– AdaptLRZapproachestoextremescaling(workshops,PiCSparnership model)



n TheUNISDRdataandcomputechallenge

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


HistoryoftheUNISDRcollaboration

n Informaldiscussionsin2014,contactthroughDRIHMprojectn Discussionre,speedingupthelosscalculation

– “Chinacalculationtakes5weeks,canyoulendusasupercomputer”n Informalcollaboration,calculationtimetofewdays

– Firstparallelversionintooperationaluse


Hazard 1 modelling

team

Hazard 1 modelling

team

Hazard N modelling

team

Hazard 1 modelling

team

Exposure modelling

team


team

Risk computation

team

GAR analysis

Nextchallenge:openGARdata


Hazard 1 modelling

team

Hazard 1 modelling

team

Hazard N modelling

team

Hazard 1 modelling

team

Exposure modelling

team


team

Risk computation

team

GAR analysis

GARopendataproduct

n Existingdemandfromthecommunity– Currentlysharedonrequest– Opendatatolowerthethresholdofreuse– Mainissue:sufficientinfrastructure

n CurrentproductionprocessdrivenbytheGARcycle– Newdocumenteverytwoyears– Simpleversioning,implicitmetadata

n Futurechallenges– On-demandprocess,multipleversions?– Morediverseuses,moreopportunitiesformisunderstandings


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(!)


GARdataportal- beta


Nextsteps

n Observetheuseofdataportal– Whowilluseit?– How?– Arethenewusecasesinadditiontoexpectedones?

n AdapttothenewGARprocess– TobedeterminedinCancunGlobalPlatformevent

n ComputeanddatasolutionforthewholeGARlifecycleprocess?


Hazard 1 modelling

team

Hazard 1 modelling

team

Hazard N modelling

team

Hazard 1 modelling

team

Exposure modelling

team


team

Risk computation

team

GAR analysis


n Conclusions

Observations related to LRZand MNM-Teamrole

n Weunderstandthepotentialimpactofadvancedenvironmentalmodelling– Andhaveamandatetopursueresearchinthedomain

n ThegapbetweentheBigData/Supercomputingpracticesandtherealityofmostofthepractitioners– Stateoftheartsolutionsmayhavehighup-frontorganisational ortechnicalinvestments

– Needtohavetheinitialsuccesstogivetimeforreflection!n LRZandMNM-Teamarepromotingenvironmental

computingasamethodtofillthisgap– ”Branding”tomakeinterestvisible


Dataandcomputechallenge

n BetweeneverydaydataandBigDataexiststhe“Awkwarddata”domain– TBrange– Typicallycollaborativeeffortswithemergingstandardsandpracticesetc).– UNISDRaperfectexample

n Computingchallengesimilar– Theroadfromasinglethreadedorsharedmemoryparallelismtoefficient

cluster/supercomputingapproachesischallenging– Effort/returnratioisveryunlinear!

n Thereisprobablynosingletechnicalsolutiontothischallenge,butorganisation/processthekey– “PiCS approachtoenvironmentalcomputing”– Workshops– Networking


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


Documents

UNISDR Global Assessment Report - Current and Emerging ... · Leibniz Supercomputing Centre (LRZ) ... SuperMUC Phase 1 + 2 ISESS -10 May 2017 M. Heikkurinen, D. Kranzlmüller 4. Top