Presented by

Visualization at the Leadership Computing Facility

Sean AhernScientific Computing

Center for Computational Sciences

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Statistical Analysis of Fusion Eddy Formation

Principal Component Analysis allows isolation of rotational modes of eddy evolution

QuickTime™ and aH.264 decompressor

are needed to see this picture.

QuickTime™ and aH.264 decompressor

are needed to see this picture.

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Visualization of Neutron Density Fields Joint work between ORNL and Oxford University has developed a

model of nuclear matter in the transitional density region between inhomogeneous matter, containing nuclei and nucleons, and homogeneous matter, consisting of uniformly distributed nucleons, in collapsing stars.

We developed custom visualization techniques for analyzing the spatially varying distribution of nucleon densities.

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Statistics and Visualization for Climate Simulation

Volume visualizations of the simulated time evolution of land and ocean components of atmospheric CO2 concentrations originating from the ocean surface were produced to help climate scientists examine the influence of climate variability, prescribed atmospheric CO2 levels, and land cover change on terrestrial carbon fluxes during the 20th century.

Data from Phase I of the CCSM LCF Computational Climate Science End Station (CCSES)

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EVEREST Facility

35 million pixel, 27-tile Powerwall 30’ x 8’ 14 NVIDIA 3000G GPUs Interactive, large-scale, collaborative data analysis Open source and custom software

DMX, Chromium, PixelBlaster, Blockbuster, etc.

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TitleTitle

Visualization Architectures for Tera/Petascale Visualization Largest datasets require use of institutional resources Reduces data movement issues Allows exploitation of multiple GPUs Provides visualization to remote users Exploited by VisIt, ParaView, EnSight

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Technology Curve Mismatches

Processor speeds (and FLOPS) are going up (60%/year)

Interconnect speeds are going up

Graphics card performance is going up (100%/year)

Increased computational ability won’t solve issues – can’t just ride processor speed curves

In some cases, users are not willing to move or post-process their data for visualization

Data Access Patterns are Important!

Memory-to-memory copy hasn’t kept up (5-10%/year)

Disk access time hasn’t kept up (5-10%/ year)

Increasing bottleneck to data processing

I/O can often be the most expensive phase of simulation or post-processing

Win Problems

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Summary and “Info-vis” techniques Increasing need to compare datasets

Parameter studies

2D/3D simulation correlations

Direct visual comparisonoften inadequate

Different mesh types, codes

High-dimensional datasets arebecoming much more common

“Information-driven” interaction Contour spectrum

Topology graphs/trees

Topology methods Distance fields

Shape characterization

Information visualization techniques

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Chromium RenderServer

Remote image delivery

OpenGL and X11 paths

Collaborative

Supports tiles

Leverages: Chromium VNC DMX

Small Business Technology Transfer program – DOE STTR

Render Server to Both Local and Remote Viewers

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Up and Coming Technologies…(and some already here)

Office “mini-clusters”

Vendor-integrated vis clusters

Very high resolution displays Sony 4K projector

Multi-GPU systems

“Cell” processor

“Commodity” constellations with GPUs

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Contact

Sean AhernScientific ComputingCenter for Computational Sciences(865) 241-3748ahern@ornl.gov

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