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Issue No. 06 - November/December (2009 vol. 29)
ISSN: 0272-1716
pp: 14-19
Kwan-Liu Ma , University of California, Davis
Scientific computing at the petascale level enables us to answer many difficult scientific questions, but the resulting data are too large to store and study directly with conventional postprocessing visualization tools. This problem will only become more severe as we reach exascale computing. A plausible, attractive solution involves processing data in situ with the simulation to reduce the data that must be transferred over networks and stored and to prepare the data for more cost-effective postprocessing visualization. The data could be reduced with compression, feature extraction, and visualization methods. This article discusses critical issues in realizing in situ visualization and data reduction and suggests important research directions.
scalability, scientific discovery, supercomputing, visualization, computer graphics

K. Ma, "In Situ Visualization at Extreme Scale: Challenges and Opportunities," in IEEE Computer Graphics and Applications, vol. 29, no. , pp. 14-19, 2009.
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