Volsh: A tool for real-time, interactive visualization

Renders images of time-evolving volumetric data . . .

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Volsh rendering


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Article courtesy Hewlett-Packard

Like scientists and engineers in many fields, climatologists crave interactive tools with which to watch their field data and simulations evolving in real time. Watching natural processes evolve over time -- whether on a scale of microseconds or millennia -- can spark pivotal insights, giving rise to an intuitive grasp of these processes that can't be readily obtained otherwise.

Such a tool has recently been developed by John Clyne, a software engineer in the Scientific Computing Division (SCD) at NCAR. Clyne has used a Hewlett-Packard Exemplar server to transform batch-mode visualization software into a real-time interactive tool.

"Graphics workstations can't do time-evolving volumetric imaging for us because hardware-accelerated systems render images by calculating polygons -- essentially triangles," says Clyne. "Typically our data represent turbulence in the atmosphere, oceans, or sun, and volumetric data of this type don't support polygonal rendering."


Steer your visualization
For this reason, he says, "No commercial product renders images of time-evolving volumetric data like ours, certainly not in real time." It all has to be done in software, such as VOLume-rendering SHell (Volsh), which relies on the computational muscle of the computer's main processors.

NCAR's vector-based supercomputers, even when running in parallel mode, don't perform such calculations interactively in real time. Most of the work that's been done in parallel volume-rendering on these computers has focused on rendering single time-steps -- a single data set essentially producing a snapshot of a dynamic process.

By computing many of these in batch mode, scientists can produce an animation of their data. But, Clyne says, "In batch mode, you run it out and hope you've got the right thing. You can look only at the result. If it's not what you want, you have to go back and do it again."

By running the new version of Volsh on the Exemplar server, NCAR scientists will be able to interact with the animation as its taking place. Clyne describes the experience: "You can sit there and steer your visualization. You can move forward or backward in time, zoom in on a feature, or change viewpoints -- observe from a different angle -- all in real time."


High performance, easy development
Despite Volsh's interactive, intense computational demands, he says, "The Exemplar server is doing everything we need it to do." The system architecture's low latencies and low overhead contribute to the high performance required to run Volsh interactively.

"Even when we use processors in multiple nodes, we see a near-linear performance increase. And we haven't done anything to our code to optimize memory layout. We just pump data into memory and let the machine handle it."

Because these data evolve through time, says Clyne, "it throws a whole new wrinkle into the problem. Now you not only need intensive computation but also intensive I/O. You've got to be able to feed the machine."

The Exemplar server provides the input/output bandwidth to handle NCAR scientists' large data sets in real time. And I/O is scalable along with Exemplar processing power, memory, and other aspects of the system.

Further, Clyne says, "The shared-memory programming model makes the Exemplar server very easy to work with." This shortens development time so NCAR scientists can begin interacting with their visualizations much sooner than would otherwise be possible.


Coming next: GUI
Clyne is developing a graphical user interface with which users will interact with the new Volsh version. When he completes this last development step, interactive Volsh will be placed in the public domain. Like earlier versions of Volsh, it will be published on the Web at www.scd.ucar.edu/vg/Software/volsh for use by other climatologists as well as by scientists and engineers in other disciplines.

"Interactive Volsh could be used in any science that must deal with enormous volumetric data sets," Clyne says.

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