SC05 November, 2005 [email protected]Supercomputing • Communications • NCAR Scientific Computing Div 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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SC05 November, 2005 [email protected] Desktop techniques for the exploration of terascale size, time-varying data sets John Clyne & Alan Norton Scientific.
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1. Improve scientist’s 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
2. Demonstrate visualization’s ability to aid in day-to-day scientific discovery process
[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.
This work is funded in part through a U.S. National Science Foundation, Information Technology Research program grant
Combination of visualization with multiresolution data representation that provide sufficient data reduction to enable interactive work on time-varying data
• Geometry Reduction (Schroeder et al, 1992; Lindrstrom & Silva, 2001;Shaffer 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
Transform
(e.g. Iso, cut plane)
Render
geometryData
Source
data Pixels
Analyze & Manipulate
Text, 2D graphics
Visualization Pipeline
Reduce Reduce
• Data reduction (Cignoni, et al 1994; Wilhelms & Van Gelder, 1994; Pascucci & Frank, 2001; Clyne 2003)
• 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
Integrated visualization and analysis on interactively selected subdomains:
u
2ur
pg
z
1 pr
1 pr
2ur
z
Vertical vorticity of the flow
Mach number of the vertical velocityFull domain seen from above Subdomain from side
Full domain seen from above Subdomain from side
Efficient analysis requires rapid calculation and visualization of unanticipated derived quantities. This can be facilitated by a combination of subdomain selection and resolution reduction.
A test of multiresolution analysis: Force balance in supersonic downflows
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.
1 pr
u
2ur
pg
z
1 pr
2ur
z
u
2ur
pg
z
1 pr
1 pr
2ur
z
Full
Half
Resolution
Subdomain selection and reduced resolution together yield data reduction by a factor of 128