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Practical use of Visualization

Practical use of Visualization. A few views from experience by a non-expert. Frank J. Seinstra Senior eScience Engineer & Executive Board Member Lead eScience Technology Platform ( eSTeP ) Netherlands eScience Center, Amsterdam, The Netherlands. Visualization @ NLeSC.

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Practical use of Visualization

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  1. Practical use of Visualization A few views from experience by a non-expert Frank J. Seinstra Senior eScienceEngineer & Executive Board Member LeadeScienceTechnology Platform (eSTeP) NetherlandseScience Center, Amsterdam, The Netherlands

  2. Visualization @ NLeSC • eScienceEngineers • translate modern ICT into research solutions • collaboratecloselywith domain experts • workboth at NLeSC and on site • scout / research / adopt / develop / makeavailablesustainableeScience tools (eSTeP) • Visualization • One of 4 maintechnologicaldirections • Currently: • 1 eScienceEngineerspecificallyforVisualization (out of approx. 15) • 1 large (500K) project; start Spring 2013 (1 extra engineer) • Ourneighbors at SURFsara: group of 3 • ‘We’ must generatemore awareness & demand!

  3. WhyVisualization? • Visualization as a Scientific Discipline • NLeSCPriorityArea: eScience Technologies • Fromhigh-performancevisualization of Big Data… • … to visualinteraction and visual data analysis • Visualizationfor PR purposes • Presentation of scientificresults & providinginsight • General outreach / futurefunding / convincingpolicy makers • Fromstudents … … to DWDD • Visualization as supportforother Disciplines • “OptimizingDiscovery in the Big Data Era” • Ampleopportunities… buthow to generateawareness/demand?

  4. eSTeP: eScienceTechnology Platform HIGH- / APPLICATION-LEVEL TOOLS (DOMAIN-SPECIFIC and GENERIC) MULTI-SCALE SIMULATION ENVIRONMENT (astrophysics, climate, …) CHEMISTRY PORTAL GENERIC HW RESERVATION SYSTEM LOW- / SYSTEM-LEVEL LIBRARIES NETWORKING COMPUTING DATA VISUALIZATION OTHER -HIGH-VOLUME DATA TRANSFER -BANDWIDTH ON DEMAND … -COMMUNICATION -COMMON ACCESS -CODE COUPLING -WORKFLOWS … -PORTAL TECHNOLOGY -NUMERICAL -PROVENANCE -REPRODUCIBILITY … -DATA MANAGEMENT -FILE SYSTEMS -SHARING -AUTHORIZATION … -DATA REPRESENTATION -DATA BROWSING -INTERACTIVITY -GUI-COMPONENTS …

  5. Jungle Computing • ‘Worst case’ computing, requiredbyend-users • Distributed • Heterogeneous • Hierachical (incl. multi- / many-cores)

  6. Why Jungle Computing? • Scientistsoftenforced to use a widevariety of resources simultaneously to solvecomputationalproblems, e.g. due to: • Desireforscalability • Distributed nature of the input data • Software heterogeneity (e.g. mix of C/MPI and CUDA) • Ad hoc hardware availability • … • Note: most users do notneed ‘worst case’ • NLeSCtechnologiesaim to apply to any subset

  7. Example 1: ComputationalAstrophysics GrandChallenge: Understanding the fundamentalphysics of the universe Demonstrated live at SC, Seattle, USA (2011) Prof. S. Portegies Zwart (Leiden); Dr. N. Drost, Drs. M. v.Meersbergen(NLeSC)

  8. Example 1: ComputationalAstrophysics • The AMUSE system (Leiden University) • Early Star Cluster Evolution, including gas • Gravitationaldynamics (N-body): GPU / GPU-Cluster • StellarEvolution: Cluster / Cloud • Hydro-dynamics, Radiative Transport: Supercomputer • Goal • Apply Jungle Computingforsimulations at unprecedentedscale gravitational dynamics stellar evolution AMUSE hydro-dynamics radiative transport

  9. Example 2: ClimateModeling GrandChallenge: Understand Impact of MeltingIce Sheets onClimateChange Demonstrated live at GLIF, Chicago, USA (2012) Prof. H. Dijkstra (IMAU); Dr. J. Maassen, Drs. M. van Meersbergen (NLeSC)

  10. Example 2: ClimateModeling • The CPL system (with Prof. H. Dijkstra, Utrecht University/IMAU) • or: The CommunityEarth System Model (CESM) • Ocean, Sea-Ice: GPU / GPU-Cluster • Atmosphere, Land-vegetation: Cluster / Cloud / Supercomputer • Goals: • Apply Jungle Computing to allowunprecedented level of detail • Advancedvisualization to analyze 100s of Terabytes of output atmosphere land-vegetation CPL sea-ice ocean

  11. Example 1: ComputationalAstrophysics Demonstrated live at SC, Seattle, USA

  12. Example 1: ComputationalAstrophysics Demonstrated live at SC, Seattle, USA

  13. Example 1: ComputationalAstrophysics Demonstrated live at SC, Seattle, USA

  14. System Visualization • ‘Serendipity’ • For evaluationpurposes: • standard set of HPC & distributedapplications • Visualizationsshowed: • oneapplication in standard set had been wrong for over 10 years! • unbalancedcommunicationpattern • Note • Visualizationnotnecessarily ‘glamorous’ • Serves taskwell: • Essential basis forbehavioralinterpretation • Provides insightforpeers (otherscientists) • Proof: “itreallyworks” • And yes… PR (butthis was not a goal)

  15. Conclusion • Visualization is… • … Science • … PR • … Support • … • … EssentialeScienceTechnology! • Opportunity / Challenge • Need to put visualizationfirmlyon the map as a core support technologyformanyscientific (Big Data) disciplines • Boost keyscientificdomains • Boost research domain of visualizationitself • How to generate / increaseawareness & demand?

  16. The End

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