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Effective large-scale data visualization remains an important challenge with analysis codes already producing terabyte results on clusters with thousands of processors. Frequently the analysis codes produce distributed data and consume a significant portion of the available memory per node. This article presents an architectural approach to handling these visualization problems based on parallel data streaming to enable visualizations on a parallel cluster. The authors' approach requires less memory than other visualizations while achieving high code reuse.
C. Charles Law, Berk Geveci, Ken Martin, James Ahrens, Kristi Brislawn, Michael Papka, "Large-Scale Data Visualization Using Parallel Data Streaming", IEEE Computer Graphics and Applications, vol. 21, no. , pp. 34-41, July/August 2001, doi:10.1109/38.933522
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