CS 677: Topics in Large Data Analysis and Visualization

Credits: 3-0-0-0 (9)

 

Prerequisites: Instructor’s consent

 

Who can take the course: PhD, Masters, 3rd and 4th year UG Students

 

Departments that may be interested: CSE, EE, BSBE, ES, CE, CHE, ME, AE, MSE, PHY, CHM

 

Course Objective

Effective analysis and visualization of large-scale data can help users to comprehend the salient patterns and features in their data quickly. Modern high-resolution scientific simulations produce gigabytes to terabytes of data. Contemporary petaflop machines result in orders of magnitude higher data production rate as compared to data consumption rate. The data generation rate will soon reach exascale. To deal with extreme-scale data, the high performance computing and visualization community has been developing novel scalable data analysis and visualization algorithms. As part of this course, we will study research papers that demonstrate big data analysis and visualization techniques from the last decade or so. This course will also focus on state-of-the-art parallel and high performance data visualization techniques. The contents of this course will be based on research papers from top-tier journals and conferences such as IEEE TVCG, CGF, IEEE/ACM Supercomputing, IEEE Visualization, IEEE TPDS, IJHPCA, IEEE LDAV, EGPGV, EuroVis and EuroGraphics, IEEE Pacific Visualization, etc.

 

 

Course Content

S.No.	Topic	Lecture hours
1	Introduction to scientific simulations and data analysis	2
2	Introduction to parallel computing	1
3	Introduction to scientific visualization	1
4	Research paper discussion on topics related to: 1. Visualization of time-varying data 2. Visualization of multivariate data 3. Flow visualization  4. Ensemble data visualization	6
5	Research paper discussion on topics related to: 5.Statistical data analysis and visualization 6.Information theory-based data analysis and visualization 7.Visualization using AI	5
6	Research paper discussion on topics related to: 8.High performance visualization 9.Performance data visualization 10.Scalable analysis and visualization	4
7	Research paper discussion on topics related to: 11.Remote visualization 12.In situ analysis and visualization 13.Resource-constrained analysis and visualization	4
8	Research paper discussion on topics related to: 14.Information visualization 15.Application-specific visualization (e.g. climate, cosmology)	3

 

 

References

 

1. In Situ Visualization for Computational Science, Springer International Publishing, 2022.
2. High Performance Visualization, Enabling Extreme-Scale Scientific Insight, Edited By E. Wes Bethel, Hank Childs, Charles Hansen, CRC Press, 2012.
3. Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, Editors: Charles D. Hansen, Min Chen, Christopher R. Johnson, Arie E. Kaufman, Hans Hagen, Springer Publications, 2014.
4. Contemporary High Performance Computing: From Petascale toward Exascale, Volume Two, Chapman & Hall/CRC, 2015.
5. Information Theory Tools for Visualization By Min Chen, Miquel Feixas, Ivan Viola, Anton Bardera, Han-Wei Shen, Mateu Sbert, by A K Peters/CRC Press, 2017.