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Issue No.04 - July/August (2001 vol.21)
pp: 34-41
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.
James Ahrens, Kristi Brislawn, Ken Martin, Berk Geveci, C. Charles Law, Michael Papka, "Large-Scale Data Visualization Using Parallel Data Streaming", IEEE Computer Graphics and Applications, vol.21, no. 4, pp. 34-41, July/August 2001, doi:10.1109/38.933522
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