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