1. Mark Rast Laboratory for Atmospheric and Space Physics Department of Astrophysical and Planetary Sciences University of Colorado, Boulder Kiepenheuer-Institut für Sonnenphysik 14 June 2006 John Clyne and Alan Norton Scientific Computing Division National Center for Atmospheric Research Boulder, Colorado VAPoR ( Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers) : Interactive analysis and visualization of very large data volumes http://www.vapor.ucar.edu/ Freely available with support. Input into future capabilities.

2. Numerical models which can currently be run on typical supercomputing platforms produce data in amounts that make storage expensive, movement cumbersome, visualization difficult, and detailed analysis impossible. The result is a significantly reduced scientific return from the largest computational efforts. 1.We can now compute more data than we can analyze. Not all technologies advance at the same rate Multiprocessor simulation vs. single/dual processor analysis 2.Most analysis tools have poor volume visualization capabilities and most visualization tools have only rudimentary analysis capabilities.

3. Example: Compressible plume dynamics 504x504x2048 5 variables (u,v,w,rho,temp) ~500 time steps saved 9TBs storage (4GBs/variable/timestep) Six months compute time required on 112 IBM SP RS/6000 processors

4. What is meant by interactive analysis? Definition: A system is interactive if the time between a user event and the response to that event is short enough maintain my full attention If the response time is… 1-5 seconds : I’m engaged 5-60 seconds : I’m tapping my foot 1-3 minutes : I’m reading email > 3 minutes : I’ve forgotten why I asked the question! Develop a tool with which one can interactively analyze and visualize very large data volumes. IO wait times for high resolution simulations: ResolutionMBs per variable Scalar variable wait time Vector variable wait time 128 3 80.10.3 256 3 670.72.1 512 3 5375.015.0 1024 3 429543.0130.0 Assumptions –Single precision –100 MB/sec bandwidth –No contention

5. Rendering timings 512 3 Compressible Convection504 2 x2048 Compressible Plume Reduced resolution affords responsive interaction while preserving all but finest features. SGI Octane2, 1x600MHz R14k SGI Origin, 10x600MHz R14k Interactive

6. Calculation timings Note: 1/2 th resolution is 1/8 th problem size, etc Deriving new quantities on interactive time scales only possible with data reduction SGI Origin, 10x600MHz R14k Interactive 512 3 Compressible Convection

7. Key VAPoR components: Multiresolution data access and subregion sampling Enable speed/quality tradeoffs Tightly coupled to existing analysis tools IDL, MatLab Advanced volume visualization tool Histogram based transfer funtion editor, Field line tracing, etc. An interactive multiresolution visualization and analysis tool.

8. Wavelet Transforms for 3D Multiresolution data representation: Hierarchical data representation Invertible and lossless (subject to floating point round off errors) Numerically efficient No additional storage cost Example: Haar Wavelet (current VAPoR format) Haar operators Store averages and differences.

9. Compressible Convection 128 3 512 3 Rast, 2002

10. Compressible plume 504x504x2048 Full 252x252x1024 1/8 126x126x512 1/64 63x63x256 1/512 Compressible plume data set shown at native and progressively coarser resolutions Resolution: Problem size: Rast, 2002

11. Sites of supersonic downflow are also those of very high vertical vorticity. The cores of the vortex tubes are evacuated, with centripetal acceleration balancing that due to the inward directed pressure gradient. Buoyancy forces are maximum on the tube periphery due to mass flux convergence. The same interpretation results from analysis at half resolution. Full Half Resolution Subdomain selection and reduced resolution together yield data reduction by a factor of 128! A test of multiresolution analysis: Force balance in supersonic downflows

12. Future Plans: Incorporate visualization techniques based on scientists’ needs –Nonuniform grids –Adaptive grids Understand effect of data compression –Error analysis and error visualization –Obtain bounds on degradation of analysis results Explore lossy data compression Improve access to terabyte datasets –Multiresolution data output as a byproduct of the simulation