Center for Computing Research (CCR)

Center for Computing Research

Scalable Analysis and Vis, 01461

The scalable analysis and visualization department specializes in the research and development of capabilities to enable an understanding of large, complex data in direct support of the broader national-security missions of Sandia National Laboratories. We develop state-of-the-art techniques for visualization and predictive analytics through deep expertise and experience in statistical methods, machine learning, data mining, high-performance computing, computational geometry and graphics. Our department also maintains a robust external presence through strategic partnerships with universities, industry, and other national laboratories; and is committed to developing and contributing to open-source technologies.

People

Ron A. Oldfield
Manager, Scalable Analysis and Vis
Email: raoldfi@sandia.gov
Phone: 505/284-9153
Fax: 505/844-4728

Mailing address:
Sandia National Laboratories
P.O. Box 5800, MS 1327
Albuquerque, NM
87185-1320
Gregory James Bristol, Office Administrative Assistant
Staff
Mark Bolstad
Patricia J. Crossno
Warren Leon Davis
Daniel Dunlavy
Matthew R. Glickman
Andrew McFarland
Kenneth D. Moreland (Ken)
Benjamin David Newton
Jeffrey Nichol
Matthew Gregor Peterson
Gabriel ANUOLUWAPO Popoola
Timothy Shead
Andrew T. Wilson

Projects

The Scalable Analysis and Visualization Department is involved with the projects listed below.

News

  • Sandia and Kitware Partner to Improve Performance of Volume Rendering for HPC Applications

    In collaboration with researchers at Sandia, Kitware developers have made significant performance improvements to volume rendering for large-scale applications. First, Kitware significantly improved unstructured-grid volume rendering....

    Sandia and Kitware Partner to Improve Performance of Volume Rendering for HPC Applications

    In collaboration with researchers at Sandia, Kitware developers have made significant performance improvements to volume rendering for large-scale applications. First, Kitware significantly improved unstructured-grid volume rendering.  In a volume-rendering example for turbulent flow with 100 million cells on 320 ranks on a Sandia cluster, the volume rendered in 8 seconds using the new method, 122 seconds for the old method, making unstructured-grid visualization a viable in-situ option for applications.  Second, Kitware created a new "resample-to-image" filter that uses adaptive-mesh refinement to calculate and resample the image to the smaller mesh with minimal visualization artifacts.  The new filter reduces the amount of data required for visualization and provides a potential performance improvement (more testing is needed).  These improvements were driven by Sandia researchers for the NNSA Advanced Simulation and Computing program in support of the P&EM, V&V, and ATDM ASC sub-elements as part of a Large-Scale Calculation Initiative (LSCI) project. Kitware funding was provided through a contract with the ASC/CSSE sub-element.  Contacts for this work are Stefan Paul Domino (spdomin@sandia.gov), W. Alan Scott (wascott@sandia.gov), and Ron A. Oldfield (raoldfi@sandia.gov).

    The image shows an unstructured volume-rendered Q-criterion field for a Reynolds # ~10,000 turbulent impinging jet. The performance improvements enabled rendering (for the first time) of the full unstructured dataset (nearly 2 billion Hexahedral elements). The rendering of this image was supported by the ASC LSCI portfolio.

    Contact: Oldfield, Ron A.
    November 2020
    2020-12237 S

  • Slycat Enables Synchronized 3D Comparison of Surface Mesh Ensembles

    In support of analyst requests for Mobile Guardian Transport studies, researchers at Sandia National Laboratories have expanded data types for the Slycat ensemble-analysis and visualization tool to include 3D surface meshes....

    Slycat Enables Synchronized 3D Comparison of Surface Mesh Ensembles

    In support of analyst requests for Mobile Guardian Transport studies, researchers at Sandia National Laboratories have expanded data types for the Slycat ensemble-analysis and visualization tool to include 3D surface meshes.  Analysts can now compare sets of surface meshes using synchronized 3D viewers, in which changing the viewpoint in one viewer changes viewpoints in all the others.  To illustrate this capability, the Slycat team performed an ensemble analysis for a material-modeling study that examines fracturing behavior in a plate after being impacted by a punch.  Input parameters include plate and punch density, friction coefficient, Young’s modulus, and initial punch velocity.  To compare different mesh variables over the same geometry, the analyst clones a mesh into multiple views, as shown in Figure 1.  The two runs represent opposite extremes for the initial punch velocity, with the 3D viewers in the top row showing the fastest initial velocity, and the viewers in the bottom row showing the slowest.  The mesh variables in the two rows are vertically matched top and bottom, so by comparing rows, you can compare the distinctly different stress behaviors of the extremes.

    This new capability represents a significant advance in our ability to perform detailed comparative analysis of simulation results.  Analyzing mesh data rather than images provides greater flexibility for post-processing exploratory analysis.

    Here we see 3 cloned viewers for each of 2 runs at timestep 400 (red and blue selected points). The clones are vertically matched between the 2 runs to display the same 3 variables: the cell-based variables of Von Mises and stress along the X-axis, and the first component of the point variable React. The top row is an example of a simulation using the fastest initial velocity value (blue scatterplot point), while the bottom row is an example of the slowest (red scatterplot point).

    Contact: Crossno, Patricia J.
    December 2020
    2020-13393R

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