This is an archival copy of the Visualization Group's web page 1998 to 2017. For current information, please vist our group's new web page.

SC 2006 LBNL Visualization Group Demos/Projects/Presentations

NERSC related projects

Posters

Booth Movie

SciDAC Movie

Gallery of Images

Title: Using VisIt to Visualize and Analyze AMR data of Turbulent Reactive Chemistry Simulations.
Description: VisIt is a free interactive parallel and distributed visualization and graphical analysis tool for viewing scientific data on Unix and PC platforms. It was designed to handle very large data set sizes and it provides readers for a variety of AMR data formats. We will show the capabilities of VisIt to visualize and analyze AMR data of Turbulent Reactive Chemistry Simulations. We will be running the demonstration in a state of the art 8 CPU, 16 core Opteron with 64GB of RAM, with the system supporting a 10TB file system connected via a 2GB/s fiber channel intertconnect.
Web page for the self-guided demo.

Title: Incite7: Plasma Wakefield Acceleration Visualization
Description: 3D visualizations of the 2006 NERSC Incite project: "Particle-in-Cell Simulation of Laser Wakefield Particle Acceleration".
Web page for the self-guided demo.

Title: Bacillus subtilis bacteriophage Phi 29 reconstruction
Description: Cryo-EM and single molecule biophysical studies of dsDNA packaging in Bacillus subtilis bacteriophage Phi 29.

Title: SciDAC2 Visualization and Analytics Center for Enabling Technologies (VACET)
Abstract: "Seeing the Unsee-able"
The Visualization and Analytics Center for Enabling Technology (VACET) focuses on the creation and deployment of scientific visualization and analytics software technology to increase scientific productivity and create new opportunities for scientific insight. The goal is to respond to the urgent needs of the scientific community by providing significant, production-quality technology to aid in data understanding.
Contact: Wes Bethel

Title: Scientific Data Analysis via Statistical Learning
Abstract: The emerging field of scientific data mining addresses the need for scientists to search for, quantify, and visualize information from massive data sets generated by large scale observations and simulations. We demonstrate the enormous potential of statistical machine learning algorithms to provide data-driven solutions to fundamental scientific questions in astrophysics and climate modeling. We present results from ongoing projects at LBNL, including the search for Type Ia supernovae from astronomical observations, and the analysis of hurricanes and tropical storms in climate simulations.
Contact: Raquel Romano