Scout A Hardware-Accelerated System for Quantitatively Driven Visualization and Analysis

• Published in: 2004 IEEE Visualization Conference pp. 171 - 178
• Main Authors: McCormick, Patrick S., Inman, Jeff, Ahrens, James P., Hansen, Charles, Roth, Greg
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 01.01.2004; IEEE
• Series: ACM Conferences
• Subjects: Applied sciences; Artificial intelligence; Computer architecture; Computer graphics; Computer science; control theory; systems; Data visualization; Exact sciences and technology; Hardware; hardware acceleration; Image analysis; Information systems. Data bases; Laboratories; Memory organisation. Data processing; multivariate visualization; Optical imaging; Pattern recognition. Digital image processing. Computational geometry; Rendering (computer graphics); Scientific computing; Software; Transfer functions; Visualization systems; volume rendering; Visualization systems; volume rendering; hardware acceleration; multivariate visualization; Capability index; Mathematical formula; Parallel languages; Visualization; Parallel algorithm; Database query; Programming language; Graphic processing unit; Transfer function; Central unit
• ISBN: 0780387880; 9780780387881
• DOI: 10.1109/VISUAL.2004.95

Quantitative techniques for visualization are critical to the successful analysis of both acquired and simulated scientific data. Many visualization techniques rely on indirect mappings, such as transfer functions, to produce the final imagery. In many situations, it is preferable and more powerful to express these mappings as mathematical expressions, or queries, that can then be directly applied to the data. In this paper, we present a hardware-accelerated system that provides such capabilities and exploits current graphics hardware for portions of the computational tasks that would otherwise be executed on the CPU. In our approach, the direct programming of the graphics processor using a concise data parallel language, gives scientists the capability to efficiently explore and visualize data sets.