November 2007
Software Enabling Technologies for Petascale Science
E. Wes Bethel, Lawrence Berkeley National Laboratory
Chris Johnson, University of Utah
Cecilia Aragon, Lawrence Berkeley National Laboratory
Prabhat, Lawrence Berkeley National Laboratory
Oliver Rübel, Lawrence Berkeley National Laboratory
Gunther Weber, Lawrence Berkeley National Laboratory
Valerio Pascucci, Lawrence Livermore National Laboratory
Hank Childs, Lawrence Livermore National Laboratory
Peer-Timo Bremer, Lawrence Livermore National Laboratory
Brad Whitlock, Lawrence Livermore National Laboratory
Sean Ahern, Oak Ridge National Laboratory
Jeremey Meredith, Oak Ridge National Laboratory
George Ostrouchov, Oak Ridge National Laboratory
Ken Joy, University of California, Davis
Bernd Hamann, University of California, Davis
Christoph Garth, University of California, Davis
Martin Cole, University of Utah
Charles Hansen, University of Utah
Steven Parker, University of Utah
Allen Sanderson, University of Utah
Claudio Silva, University of Utah
Xavier Tricoche, University of Utah


We clearly don’t want to present an image of the entire dataset at each timestep – the result would be a very cluttered and unintelligible display. Instead, we want to offer the ability for a fusion scientist to focus visual analysis on subsets of data. The result, which is shown below in Figure 4, is an effective context-and-focus interface for rapidly selecting subsets of particles for display.

Figure 4

Figure 4. For visual exploration and analysis of GTC data, our implementation provides a visual interface for selecting subsets of particles that meet a set of user-defined criteria. Here, “interesting” is defined as those data points that satisfy a set of multivariate range combinations via the parallel coordinates interface (lower image). The subset satisfying these range conditions then appears in the physical view (top image), where the view may be manipulated, the color transfer changed to draw attention to specific particles based upon other user-defined criteria, or subject to other types of visual or traditional analysis. (Image courtesy S. Ahern, Oak Ridge National Laboratory)

These concepts can be applied to other types of data in other scientific domains, such as exploring the relationships between gene expression levels in cells of a developing organism as shown in Figure 5. These ideas, when combined with multiple linked views where updates in one display are then propagated to other views of the same dataset, offer an extremely powerful framework for rapid exploration of complex data.12

Figure 5

Figure 5. Here, we define three groups of “interesting” data – colored red, green, and blue – with a parallel coordinates interface (bottom pane), and those data points satisfying the multivariate range condition appear in the physical view (upper right). Here, we introduce a third linked view (upper left) that shows a 3D scatterplot – each of the data points from the three “groups of interesting” are colored red, green or blue (according to which “group of interesting” they belong). The three axes of the scatterplot are the expression levels for three specific genes; there are on the order of about 20 different gene expression levels per cell in this dataset – we picked three genes from the group of 20 for the purposes of display. This type of linked view presentation is very helpful in conveying different types of relationships in complex data. (Image courtesy of Oliver Rübel, Lawrence Berkeley National Laboratory)

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Reference this article
Bethel, E. W., Johnson, C., Aragon, C., Prabhat, Rübel, O., Weber, G., Pascucci, V., Childs, H., Bremer, P.-T., Whitlock, B., Ahern, S., Meredith, J., Ostrouchov, G., Joy, K., Hamann, B., Garth, C., Cole, M., Hansen, C., Parker, S., Sanderson, A., Silva, C., Tricoche, X. "DOE's SciDAC Visualization and Analytics Center for Enabling Technologies - Strategy for Petascale Visual Data Analysis Success," CTWatch Quarterly, Volume 3, Number 4, November 2007. http://www.ctwatch.org/quarterly/articles/2007/11/does-scidac-visualization-and-analytics-center-for-enabling-technologies-strategy-for-petascale-visual-data-analysis-success/

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