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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. C. Law, B. Geveci, K. Martin, J. Ahrens, K. Brislawn and M. Papka, "Large-Scale Data Visualization Using Parallel Data Streaming," in IEEE Computer Graphics and Applications, vol. 21, no. , pp. 34-41, 2001.
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