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Visualization Challenges at Scale

Visualization Challenges at Scale. MSIPP Meeting. May 30, 2012. VisIt Team. Introducing VisIt. VisIt is a free software application for visualizing and analyzing terascale and petascale simulation datasets. Begun Summer 2000 R&D 100 award winner in 2005 Used worldwide

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Visualization Challenges at Scale

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  1. Visualization Challenges at Scale MSIPP Meeting • May 30, 2012 • VisItTeam

  2. Introducing VisIt • VisIt is a free software application for visualizing and analyzing terascale and petascale simulation datasets • Begun Summer 2000 • R&D 100 award winner in 2005 • Used worldwide • Target use cases: • Quantitative Analysis • Comparative Analysis • Data Exploration • Visual Debugging • Presentation Graphics 3K^3 (27 Bz dataset)

  3. Visualization Tool Architecture • Server runs remotely in parallel, handling data processing for client • Clients runs locally and display results computed on the server Local VisIt Clients Parallel Cluster Files Vis Server Data Plugin Data • Vis Server Data Plugin MPI Data network connection Vis Server Data Plugin Data Vis Server Filter • Data processed in data flow networks • Filters in data flow networks can be implemented as plug-ins Filter Data Flow Network Filter

  4. VisIt scales well on current HPC platforms Scaling Studies of Isosurface Extraction and Volume Rendering (2009) Real Story: Success leveraged favorable I/O + Memory characteristics

  5. Future Machines are Different • Roughly 104to 105 cores • 1Gb to 2Gb of memory per core • Time to write system checkpoint: 300-1500 sec • CPU only Today Tomorrow Major concerns on the path to Exascale: • Data Movement (I/O, Communication) • Memory Constraints • Programming Models • MPI / thread hybrid parallelism • Accelerator Architectures • Dynamic / Redundant work assignment • Reliability • Power Consumption • LLNL’s Sequoia has 1.6 million cores. Exascale may have up to 109 cores • Less than 1Gb of memory per core • Not feasible to write system checkpoint • CPU, GPU or other accelerators, massive threading

  6. Future Platforms Demand New Techniques Multi-Res Data Reduction In Situ Out-of-core Remaining ~5% on SC w/ `Pure Parallelism’ Hypothetical distribution of visualization and analysis work.

  7. In Situ as a Solution to I/O • VisIt provides Libsim, a library that simulations may use to let VisIt connect and access their data • Share simulation’s arrays to avoid I/O and data movement Libsim Front End Simulation Libsim Front End VisIt Runtime Source Adaptor Data Access Code Filter Data Filter

  8. Sequoia Will Drive Libsim Improvements Sequoia is being built right now at LLNL • ASC codes are seeking to run at 100K’s of cores • Increased role for in situ • Alternate libsim implementation • Reuse libsim front end and adaptor • lightweight plotting back end, maybe custom to the simulation • Static linking Statically linked Dynamically loaded Libsim Front End Simulation VisIt runtime Adaptor output Statically linked Libsim Front End Simulation Lightweight Plotting Adaptor output

  9. Efforts That Would Improve VisIt • Improved I/O performance • Lower memory usage and live within certain amount of memory • Lighter weight in situ solutions • Data reduction and simplification • Multiresolution data • Introduce support for hybrid parallelism • MPI + threading • MPI + threading + accelerators

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