SC05

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Desktop techniques for the exploration of
terascale size, time-varying data sets

• John Clyne Alan Norton
• Scientific Computing Division
• National Center for Atmospheric Research
• Boulder, CO USA

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National Center for Atmospheric Research
More than just the atmosphere from the earths
oceans to the solar interior
Turbulence
The Sun
Space Weather
Atmospheric Chemistry
Climate
Weather
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Goals

• Improve scientists ability to investigate and
  understand complex phenomena found in
  high-resolution fluid flow simulations
• Accelerate analysis process and improve
  scientific productivity
• Enable exploration of data sets heretofore
  impractical due to unwieldy size
• Gain insight into physical processes governing
  fluid dynamics widely found in the natural world
• Demonstrate visualizations ability to aid in
  day-to-day scientific discovery process

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Problem motivationAnalysis of high resolution
numerical turbulence simulations

• Simulations are huge!!
• May require months of supercomputer time
• Multi-variate (typically 5 to 8 variables)
• Time-varying data
• A single experiment may yield terabytes of
  numerical data
• Analysis requirements are formidable
• Numerical outputs simulate phenomena not easily
  observed!!!
• Interesting domain regions (ROIs) may not be
  known apriori
• Additionally
• Historical focus of computing centers on batch
  processing
• Dichotomy of batch and interactive processing
  needs
• Currently available analysis tools inadequate for
  large data needs
• Single threaded, 32bit, in-core algorithms
• Lack advanced visualization capabilities
• Currently available visualization tools
  ill-suited for analysis

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And furthermore
Numerical models that 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
nation's largest computational efforts.
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A sampling of various technology performance
curves

• Not all technologies advance at same rate!!!

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Example Compressible plume dynamics

• 504x504x2048
• 5 variables (u,v,w,rho,temp)
• 500 time steps saved
• 9TBs storage
• Six months compute time required on 112 IBM SP
  RS/6000 processors
• Three months for post-processing
• Data may be analyzed for several years

M. Rast, 2004. Image courtesy of Joseph Mendoza,
NCAR/SCD
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Visualization and Analysis Platform for oceanic,
atmospheric, and solar Research (VAPoR)

• Key components
• Domain specific
• numerically simulated turbulence in the natural
  sciences
• Data processing language
• Data post processing and quantitative analysis
• Advanced visualization
• Identify spatial/temporal ROIs
• Multiresolution
• Enable speed/quality tradeoffs

Combination of visualization with multiresolution
data representation that provide sufficient data
reduction to enable interactive work on
time-varying data
This work is funded in part through a U.S.
National Science Foundation, Information
Technology Research program grant
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Multiresolution Data Representation
geometry
data
Pixels
Render
Visualization Pipeline

• Geometry Reduction (Schroeder et al, 1992
  Lindrstrom Silva, 2001Shaffer and Garland,
  2001)

• Wavelet based progressive data access
• Mathematical transforms similar to Fourier
  transformations
• Invertible and lossless
• Numerically efficient forward and inverse
  transform
• No additional storage costs
• Permit hierarchical representations of functions
• See Clyne, VIIP2003

• Data reduction (Cignoni, et al 1994 Wilhelms
  Van Gelder, 1994 Pascucci Frank, 2001 Clyne
  2003)

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Putting it all together

• Visual data browsing permits rapid identification
  of features of interest, reducing data domain
• Multiresolution data representation affords a
  second level of data reduction by permitting
  speed/quality trade offs enabling rapid
  hypothesis testing
• Quantitative operators and data processing enable
  data analysis
• Result Integrated environment for large-data
  exploration and discovery

• Goal Avoid unnecessary and expensive full-domain
  calculations
• Execute on human time scales!!!

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Compressible Convection
M. Rast, 2002
1283
5123
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Compressible plume
Compressible plume data set shown at native and
progressively coarser resolutions
504x504x2048 Full
252x252x1024 1/8
126x126x512 1/64
63x63x256 1/512
Resolution Problem size
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Rendering timings
5123 Compressible Convection
5042x2048 Compressible Plume
SGI Octane2, 1x600MHz R14k
SGI Origin, 10x600MHz R14k
Reduced resolution affords responsive interaction
while preserving all but finest features
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Derived quantities
Derived quantities produced from the simulations
field variables as a post-process

• ?? Avogadros number
• me electron mass
• k Boltzmanns constant
• h Plancks constant

• p pressure
• ? density
• T temperature
• ?? ionization potential

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Calculation timings for derived quantities
SGI Origin, 10x600MHz R14k
Note 1/2th resolution is 1/8th problem size, etc
Deriving new quantities on interactive time
scales only possible with data reduction
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Error in approximations
Resolution P Eq 1 Y Eq 2 ?2 Eq 3
Full 0 0 0
1/2 1.09 0.03 85.57
1/4 2.53 0.14 97.3
1/8 3.79 0.65 99.8

• Error is highly dependent on operation performed
• Algebraic operations tested introduced low error
  even after substantial coarsening
• Error grows rapidly for gradient calculation
• Point-wise error gives no indication of global
  (average) error

Point-wise, normalized, maximum, absolute error
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Integrated visualization and analysis on
interactively selected subdomains
Mach number of the vertical velocity
Efficient analysis requires rapid calculation and
visualization of unanticipated derived
quantities. This can be facilitated by a
combination of subdomain selection and resolution
reduction.
Vertical vorticity of the flow
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A test of multiresolution analysis Force
balance in supersonic downflows
Resolution
Full
Half
Subdomain selection and reduced resolution
together yield data reduction by a factor of 128
Sites of supersonic downflow are also those of
very high vertical vorticity. The core 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.
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Summary

• Presented prototype, integrated analysis
  environment aimed at aid investigation of
  high-resolution numerical fluid flow simulations
• Orders of magnitude data reduction achieved
  through
• Visualization Reduce full domain to ROI
• Multiresolution Enable speed/quality trade-offs
• Coarsened data frequently suitable for rapid
  hypothesis testing that may later be verified at
  full resolution

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Future work

• Quantify and predict error in results obtained
  with various mathematical operations applied to
  coarsened data
• Investigate lossy and lossless data compression
• Add support for less regular meshes
• Explore other scientific domains
• Climate, weather, atmospheric chemistry,

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Future???
Original
201 Lossy Compression
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Acknowledgements

• Steering Committee
• Nic Brummell - CU, JILA
• Aimé Fournier NCAR, IMAGe
• Helene Politano - Observatoire de la Cote d'Azur
• Pablo Mininni, NCAR, IMAGe
• Yannick Ponty - Observatoire de la Cote d'Azur
• Annick Pouquet - NCAR, ESSL
• Mark Rast - NCAR, HAO
• Duane Rosenberg - NCAR, IMAGe
• Matthias Rempel - NCAR, HAO
• Yuhong Fan - NCAR, HAO

• Developers
• Alan Norton NCAR, SCD
• John Clyne NCAR, SCD
• Research Collaborators
• Kwan-Liu Ma, U.C. Davis
• Hiroshi Akiba, U.C. Davis
• Han-Wei Shen, Ohio State
• Liya Li, Ohio State
• Systems Support
• Joey Mendoza, NCAR, SCD

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Questions???

• http//www.scd.ucar.edu/hss/dasg/software/vapor