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Open-source software that opens eyes

VAPOR breaks down barriers to large-scale science

by Marijke Unger

Data sets on the order of terabytes (trillion of bytes) aren’t easy to explore in an interactive fashion without an armada of hardware and software. But a new platform developed by NCAR and two university partners offers users a comprehensive desktop environment for analyzing massive data sets, allowing them to see both the forest for the trees.

VAPOR, the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Research, is an open-source software tool developed by NCAR’s Computational and Information Systems Laboratory (CISL) in partnership with the University of California at Davis and Ohio State University. VAPOR is made possible through support from NSF’s Information Technology Research for National Priorities program.

Designed to render visualizations from numerical data, VAPOR makes it possible for researchers literally to see the meaning in data sets that are often too large or complex to analyze in any other way. Scientists are using VAPOR to explore simulations for cubes as large as 15363 grid points. That’s 3.6 billion grid points and about 15 gigabytes of storage for a single variable.

The four-step image below, produced by NCAR’s Pablo Mininni and colleagues using VAPOR, shows progressive zooms in a 3-D rendering of the intensity of vorticity (circulation) in a very-high-resolution simulation of hydro­dynamic turbulence. The image allows scientists to see vortex filaments (elongated structures with strong vorticity) as well as their clustering into larger-scale patterns.

Before the advent of VAPOR, interactively exploring such a large data set would require a “brute force” approach, using parallel visualization software and dozens of connected workstations. Proceeding through multiple time steps would demand a visual supercomputer comprised of hundreds of nodes and an equally substantial storage system capable of delivering tens of gigabytes of data per second. What makes VAPOR unique is its capacity to render a big-picture view and explore smaller areas of interest, all from the convenience of a single desktop or laptop computer.

Analogous to the way that Google Earth lets users zoom in from a low-Earth orbit all the way down to their driveways, VAPOR can generate a 3-D visualization from an enormous collection of data, then allow the user to zoom in on particular features for closer study (see graphic). And VAPOR lets researchers analyze high-resolution data at a distance, without having to download it from the supercomputing facilities where it is generated or stored.

“We wanted to create a tool that could be used by scientists rather than visualization experts,” says John Clyne, who leads CISL’s VAPOR efforts. The idea, he adds, was for “an analysis tool as opposed to a visualization tool, but one where visualization was a significant part of the analysis process.”

Getting a handle on turbulence

VAPOR’s roots lie in studies of turbulence, which underlies phenomena as diverse as Earth’s magnetosphere, salinity gradients that drive ocean circulation, and daily weather. To understand the dynamics of the atmosphere and oceans, the Sun, and solar-terrestrial interactions, physical scientists find it essential to investigate relevant turbulent processes at a fundamental level—which often takes massive simulations.

The Institute for Mathematics Applied to Geosciences (IMAGe), part of CISL, works extensively on turbulence ­problems. The group created a large magnetohydrodynamic (MHD) ­simulation at very high resolution, using a code developed and maintained at NCAR and run in collaboration with several groups in the United States, France, England, and Argentina. MHD simulations are used to understand the dynamics of solar magnetic fields, solar winds, Earth’s core, and space weather. IMAGe’s simulation—the largest numerical experiment of its kind—illustrated for the first time the self-similar (fractal-style) growth of maxima in the formation, rolling, and stretching of vorticity (circulation) and current sheets.

Front-page treatment: VAPOR-produced imagery was featured on the 30 November 2007 cover of Physical Review Letters and earlier this year in a 10th Anniversary Highlights compilation from New Journal of Physics. The PRL cover image portrays a wavy spatial distribution of magnetic energy and field lines from magnetohydrodynamic simulations carried out by Yannick Ponty of France’s Côte d’Azur Observatory. For New Journal of Physics, Mark Rast (University of Colorado at Boulder) and NCAR’s Pablo Mininni worked with VAPOR’s John Clyne and Alan Norton to produced the image at top center, showing a downflowing circulation within turbulent convection, and center right, depicting a snapshot of pressure fluctuation within the head of a 3-D compressible plume. (Cover images courtesy American Physical Society and American Institute of Physics/Deutsche Physikalische Gesellschaft.)

IMAGe’s Pablo Mininni used VAPOR to look at areas of the MHD simulation in closer detail. “We were able to find structures that we wouldn’t have found in any other way. One of the nicest features was being able to navigate in an interactive way and look at tiny structures in this enormous cube, without having to wait a long time to see what the image looks like. It really facilitates the discovery process,” he says.

At Oregon State University, William Smyth and graduate student Satoshi Kimura used VAPOR to study the role of turbulence in the mixing of seawater. This crucial aspect of ocean circulation and climate is complicated by the fact that heat and salt diffuse at very different rates. A glass half-filled with cold water and half with warm water will mix within a few minutes, notes Smyth. However, if the same glass is filled with equal ­portions of salty and fresh water, the salinity difference will persist for many days because the salt gradients diffuse very slowly.

The upshot: in order to simulate ocean mixing, one must use fine enough resolution to capture the millimeter-scale salt gradients, while covering a volume of water large enough for turbulence to develop. “This computational problem was considered impossible until last year,” says Smyth. His and Mininni’s projects, plus several others, were made possible through the Breakthrough Science program, designed by NSF and NCAR to facilitate scientific discovery through very large allocations of resources associated with the arrival of NCAR’s blueice supercomputer in late 2006. (CISL recently announced a similar program, Accelerated Scientific Discovery, calling on the lab’s latest supercomputer, bluefire, which is being installed and tested this spring. Applicants for the program are being accepted through 21 May; see “On the Web.”)

“With the supercomputers at NCAR, we can do much more realistic ­simulations,” says Smyth. However, he adds, analyzing such simulations is no piece of cake. “You have to ask a lot of questions, and it’s no good if it takes months to get every answer. By then, you’ve forgotten the question!”
One way that VAPOR makes large data sets more tractable is through wavelet transforms—mathematical transformations that facilitate representing the data in a compact fashion. With VAPOR’s wavelet transforms, according to Smyth, “the size of the data set is reduced while keeping most of the essential information, so you can get answers on time scales of minutes, just like with ­workstation-sized problems.”

Onward to weather

VAPOR depicts cold air sweeping southward across Georgia by tracing particle motion based on output from the Weather Research and Forecasting model. (Image courtesy VAPOR and Thara Prabhakaran, University of Georgia.)

While the early versions of VAPOR targeted physicists who study turbulence, the software is being enhanced to meet the needs of the more general Earth and space sciences community. For example, researchers are developing capabilities for use with the multiagency Weather Research and Forecasting (WRF) modeling system, which is now used widely at global prediction centers as well as research labs. CISL has surveyed WRF users and is planning to tailor several new features for them, including two-dimensional data slices as well as colored ­surfaces that depict constant values of temperature or other weather variables.

Weather modelers may also find some of the turbulence-oriented features of VAPOR useful, according to CISL’s Alan Norton. For example, VAPOR can display moving images of fluid motion using a technique called Image-Based Flow Visualization. With this tool, weather researchers have been able to observe the multiplicity of vortices emerging along the front edge of a line of severe thunderstorms.

Two major new versions of VAPOR were completed and released in the last year, and more than 4,000 copies of the software have been downloaded by research groups worldwide. VAPOR-produced imagery has already appeared in a number of scientific publications and presentations.
VAPOR’s developers are looking ahead to future editions. Preliminary research in more aggressive data reduction techniques has been promising. Such research is the cornerstone to another key area of future development: preparing VAPOR for petascale computing.

“VAPOR is breaking down some of the barriers to large-scale science,” says Clyne, “making it possible to explore vast data sets without the need for Herculean computing resources.” ♦


On the Web


2008 Accelerated Scientific Discovery at NCAR



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