CS2013-final-report

7. Apply standard numerical algorithms to solve ODEs and PDEs. Use computing systems to solve systems of
equations. [Usage]
8. Describe the basic properties of bandwidth, latency, scalability and granularity. [Familiarity]
9. Describe the levels of parallelism including task, data, and event parallelism. [Familiarity]
10. Compare and contrast parallel programming paradigms recognizing the strengths and weaknesses of each.
[Assessment]
11. Identify the issues impacting correctness and efficiency of a computation. [Familiarity]
12. Design, code, test and debug programs for a parallel computation. [Usage]
CN/Interactive Visualization
[Elective]
This sub-area is related to modeling and simulation. Most topics are discussed in detail in other
knowledge areas in this document. There are many ways to present data and information,
including immersion, realism, variable perspectives; haptics and heads-up displays, sonification,
and gesture mapping.
Interactive visualization in general requires understanding of human perception (GV/Basics);
graphics pipelines, geometric representations and data structures (GV/Fundamental Concepts);
2D and 3D rendering, surface and volume rendering (GV/Rendering, GV/Modeling, and
GV/Advanced Rendering); and the use of APIs for developing user interfaces using standard
input components such as menus, sliders, and buttons; and standard output components for data
display, including charts, graphs, tables, and histograms (HCI/GUI Construction, HCI/GUI
Programming).
Topics:
• Principles of data visualization
• Graphing and visualization algorithms
• Image processing techniques
• Scalability concerns
Learning Outcomes:
1. Compare common computer interface mechanisms with respect to ease-of-use, learnability, and cost.
[Assessment]
2. Use standard APIs and tools to create visual displays of data, including graphs, charts, tables, and
histograms. [Usage]
3. Describe several approaches to using a computer as a means for interacting with and processing data.
[Familiarity]
4. Extract useful information from a dataset. [Assessment]
5. Analyze and select visualization techniques for specific problems. [Assessment]
6. Describe issues related to scaling data analysis from small to large data sets. [Familiarity]
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