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Mark Rast Laboratory for Atmospheric and Space Physics
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Transcript of Mark Rast Laboratory for Atmospheric and Space Physics
Mark RastLaboratory for Atmospheric and Space PhysicsDepartment of Astrophysical and Planetary SciencesUniversity of Colorado, Boulder
Kiepenheuer-Institut für Sonnenphysik14 June 2006
John Clyne and Alan NortonScientific Computing DivisionNational Center for Atmospheric ResearchBoulder, 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.
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.Performance gains from 1980 to present
1
10
100
1000
10000
100000
1980198219841986198819901992199419961998200020022004
Improvement
Disk Drive Internal DataRate
Disk Drive InterfaceData Rate
Ethernet NetworkBandwidth
Intel MicroprocessorClock Speed
Drive Capacity
• 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.
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
QuickTime™ and a decompressor
are needed to see this picture.
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: Resolution MBs per variable
Scalar variable wait time
Vector variable wait time
1283 8 0.1 0.3
2563 67 0.7 2.1
5123 537 5.0 15.0
10243 4295 43.0 130.0
• Assumptions– Single precision– 100 MB/sec bandwidth– No contention
Rendering timings
0.1
1
10
100
1000
Full 1/2 1/4 1/8
Resolution
Time in seconds
Mdb
Vtk
0.01
0.1
1
10
Full 1/2 1/4 1/8
Resolution
Time in seconds
Mdb
5123 Compressible Convection 5042x2048 Compressible Plume
Reduced resolution affords responsive interaction while preserving all but finest features.
SGI Octane2, 1x600MHz R14k
SGI Origin, 10x600MHz R14k
Interactive
Calculation timings
0.01
0.1
1
10
100
1000
10000
Full 1/2 1/4 1/8
Resolution
Time in Seconds
pressure (eq 1)
ionization (eq 2)
enstrophy (eq 3)
Note: 1/2th resolution is 1/8th problem size, etc
Deriving new quantities on interactive time scales only possible with data reduction
SGI Origin, 10x600MHz R14k
Interactive5123 Compressible Convection
Key VAPoR components: Multiresolution data access and subregion sampling
Enable speed/quality tradeoffs
Tightly coupled to existing analysis toolsIDL, MatLab
Advanced volume visualization toolHistogram based transfer funtion editor, Field line tracing, etc.
An interactive multiresolution visualization and analysis tool.
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)
Haaroperators xxU
xxP
2
1)(
)(
=
=
Store averages and differences.
Compressible Convection
1283 5123Rast, 2002
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
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.
1 prρ
∂∂
uρΗ−∇ ⋅
2urθ
pg
zρ∂
− +∂
1 prρ
∂∂
2urθ
zω−
uρΗ−∇ ⋅
2urθ
pg
zρ∂
− +∂
1 prρ
∂∂
1 prρ
∂∂
2urθ
zω−
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
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