| Code |
15634
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| Year |
1
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
Programming skills.
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Learning outcomes |
General objectives:
- 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.
- To expose students to visual representation methods and techniques that increase the understanding of complex data.
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 be able to design and develop and implement a compute shader to run general-purpose tasks in parallel on GPU.
- To be able to design and develop a CUDA-based interactive application.
- To be able to design and develop a numeric algorithm on GPU.
- To be able to design and develop a scientific visualization application that take advantage of shader programming and/or CUDA programming.
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Syllabus |
Part I: Shader Programming
01. Basics of shader programming in GLSL.
02. Illumination, shading, and textures in GL SL.
03. Shaders for image processing and screen image techniques.
04. Tessellation shaders and shadows.
05. Compute shaders.
Part II: GPU Computing
06. GPU architecture.
07. CUDA programming techniques.
08. Re-design of fundamental algorithms in CUDA.
09. CUDA programming: advanced topics.
10. Integrating OpenGL, GLSL, and CUDA.
Part III: Data Visualization
11. Visualization fundamentals.
12. Visualizing abstract data.
13. Visualizing spatial data.
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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, 200 8.
- 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.
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Teaching Methodologies and Assessment Criteria |
To allow the student to acquire the skills required in course, the following activities are planned:
- theoretical (T) lectures on theoretical concepts, methods and algorithms, using overhead projection, white-board writing, and discussing ideas with students;
- practical and laboratory classes (PL), in which students apply and test concepts and algorithms introduced in lectures by solving programming exercises proposed by the instructor;
- tutoring for answering questions, solving problems, as well as to monitor the students in developing their individual projects;
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Language |
Portuguese. Tutorial support is available in English.
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