Visualizing Non-Spatial Data

Presenters: Mark Van Moer, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign; and Rob Sisneros,

National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign

Date: June 7, 2017



Traditional HPC visualization has focused on physical simulation data, e.g., fluid mechanics, geosciences, molecular dynamics, astrophysics, and so on. Tools such as VisIt, ParaView, and IDL support these kinds of data very well. With the recent growth of data science there has been an increased need for visualizing data without inherent spatial coordinates. That is, a spatial organization has to be imposed upon these kinds of data, resulting in what is commonly called information visualization. In this session, we will present an overview of how VisIt, ParaView and other tools available on Blue Waters support information visualization.

Target Audience: Those generating non spatial data.

Prerequisites: Some programming experience would help.


Mark W. Van Moer is a Senior Visualization Programmer at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. His scientific visualization work focuses on finite element meshes, in-situ visualization, remote visualization, and the integration of visualization into larger HPC workflows. His information visualization work touches on text analysis, multi-dimensional, and hierarchical data. He is interested in introducing visualization to new communities and disciplines.

Robert Sisneros is the Technical Program Manager for the Data Analysis and Visualization Group at the National Center for Supercomputing Applications. This group is tasked with supporting science teams utilizing NSF HPC resources as well as furthering the state of scientific visualization through cutting edge research. As a senior member of the Blue Waters Project, Sisneros’s research interests in I/O and visualization are primarily aligned with issues of particular importance to high performance computing. These include: in situ visualization, data models and representations, parallel analysis algorithms, I/O parameter optimization, and “big data” analytics. Sisneros earned the degrees of Bachelor of Science in Mathematics and Computer Science from Austin Peay State University and the degrees of Master of Science and Doctor of Philosophy in Computer Science from the University of Tennessee in Knoxville.