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

Adaptive Mesh Refinement Visualization

Adaptive Mesh Refinement (AMR) techniques combine the compact, implicitly specified structure of regular, rectilinear with the adaptivity to changes in scale of unstructured grids. AMR has proven particularly useful for modeling multiscale computational domains that span many orders of magnitude of spatial or temporal scales by focusing solvers on regions where “interesting” physics or chemistry occur. Such domains include applications like astrophysics supernova modeling, where the simulation endeavors to model phenomena that occur at scales ranging from sub-kilometer to interplanetary. AMR avoids the inefficiencies inherent in attempting to model this vast computational domain at a single, fine, homogeneous resolution.

Handling AMR data for visualization is challenging, since coarser information in regions covered by finer patches is superseded and replaced with information from these finer patches. During visualization, it becomes necessary to manage the selection of which resolutions are being used. Furthermore, it is difficult to avoid discontinuities at level boundaries, which, if not properly handled, lead to visible artifacts in visualizations. Due to these difficulties, AMR support as first class data type in production visualization tools has been lacking despite the growing popularity of AMR-based simulations.17

Figure 6

Figure 6. Production-quality visualization of data from an AMR-based simulation of a hydrogen flame. The left panel shows three orthogonal slices colored by temperature (blue is colder, red is hotter). The right panel shows an image produced with volume rendering of the same variable from this dataset. (Simulation data courtesy M. Day and J. Bell, Lawrence Berkeley National Laboratory; images courtesy G. Weber, Lawrence Berkeley National Laboratory).

Through interactions with our computational science stakeholders, VACET is providing production-quality, parallel capable software providing capabilities that fulfills needs in exploratory, analytical and presentation AMR visualization. Our deployment software – VisIt18 – is an open source visualization tool that accommodates AMR as a first class data type. VisIt handles AMR data as a special case of “ghost data,” i.e., data that is used to make computations more efficient, but which is not considered to be part of the simulation result. VisIt tags cells in coarse patches that are available at finer resolution as “ghost” cells, allowing AMR patches to retain their highly efficient native format as rectilinear grids. VisIt offers a rich set of production-quality functions, such as pseudocolor and volume rendering plots (Figure 6), for visualization and analysis of massive scale data sets, making it an ideal candidate to replace specialized AMR visualization tools.

Figure 7

Figure 7. Spreadsheet plots are an important tool for debugging AMR codes. They support direct viewing of numerical data in patch cells. VisIt labels selected cells both in Spreadsheet and 3D visualizations allowing users to recognize correspondences quickly and effectively.

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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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