|
Learning outcomes |
The general objectives of the course are:
- To enable students with a holistic view of computer graphics, scientific computing, and data visualization.
- To enable students with skills in shader programming.
- To enable students with skills in GPU parallel computing (e.g., compute shaders and CUDA).
- To expose students to visual representation methods and techniques that increase the understanding of complex data.
- To prepare students for the research at MSc and PhD levels.
Regarding learning objectives, at the end of course the student must at least to be able:
- To re-program the 3D graphics pipeline using a shader (e.g., Blinn-Phong shader).
- To design and develop and implement a compute shader to run general-purpose tasks in parallel on GPU.
- To design and develop a CUDA-based interactive application.
- To design and develop a scientific visualization application (e.g., molecular visualization) using shader programming and/or CUDA programming.
|
|
Main Bibliography |
Principal/Main:
- D. Wolf. OpenGL 4 Shading Language Cookbook, 2nd ed., PACKT Publishing, 2013.
- Shane Cook, CUDA Programming: A Developer's Guide to Parallel Computing with GPUs, Morgan Kaufmann, 2013.
- T. Munzner. Visualization Analysis and Design, CRC Press, 2014.
- R. Grant. Data Visualization: Charts, Maps, and Interactive Graphics, CRC Press, 2018.
Complementar/Complementary:
- G. Sellers, R. Wright Jr., and N. Haemel. OpenGL SuperBible, 7th ed., Addison-Wesley Professional, 2015.
- T. Akenine-Moller, E. Haines, and N. Hoffmann. Real-Time Rendering, 3rd ed,. A.K. Peters / CRC Press, 2008.
- D. Kirk and W. Hwu. Programming Massively Parallel Processors: A Hands-On Approach, Morgan Kaufmann Publishers, 2010.
- J. Sanders and E. Kandrot. CUDA by Example: An Introduction to General-Purpose GPU Programming, Addison-Wesley Professional, 2011.
- S. Murray. Interactive Data Visualization for the Web, O’Reilly, 2013.
- I. Meirelles. Design for Information, Rockport Publishing, 2013.
|