As the size of data generated from numerical simulations continues to increase, visualization is now playing an increasingly more important role in assisting the scientists to obtain insight into the simulation output. To equip students with the ability to analyze very large-scale data sets, this course will provide an in-depth discussion of the state-of-the-art in large scale scientific visualization algorithms and systems. In addition to the fundamental visualization techniques, we will cover parallel implementation of selected algorithms for high-performance architectures such as the Blue Waters supercomputer. Students will get hands-on experience visualizing large-scale scientific data sets.
This course will include video lectures, quizzes, and homework assignments and will provide students with free access to the Blue Waters supercomputer. The course is intended for graduate students in computer science or areas related to computational sciences who are interested in learning how to use visualization to analyze large-scale scientific data sets and also will be of interest to students who are considering scientific visualization as a research topic for their advanced studies.
The instructor is Dr. Han-Wei Shen, professor of Computer Science and Engineering at the Ohio State University. A local instructor at the Cyprus Institute will be responsible for advising the local students and officially assigning grades. Students will complete the online course exams and exercises as part of their grade.
Prerequisites for participating graduate students include:
- Experience working in a Unix environment
- Experience developing and running codes written in C or C++
- Knowledge in 3D computer graphics and OpenGL/GPU programming is recommended
- Knowledge in parallel programming tools such as MPI is recommended
I. Visualization Foundation (3 weeks)
Scientific data representation and file format
Overview of visualization software (VTK and ParaView)
Overview of visualization Pipeline and parallelization strategies
II. Visualization Algorithms (6 weeks)
Scalar field visualization (volume rendering, isosurface, and topological methods)
Vector and tensor field visualization (streamline, pathline, stream surfaces)
III. Large Scale Data Visualization and Analysis (6 weeks)
Time-Varying data visualization
Statistics and query driven visualization
Feature extraction and tracking
In situ visualization
- Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 4th Edition, Schroeder, Martin, Lorensen
- The Visualization Handbook: Hansen, Johnson
- High Performance Visualization: Enabling Extreme-Scale Scientific Insight, Bethel, Childs, Hansen