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Effective large-scale data visualization remains an important challenge with analysis codes already producing terabyte results on clusters with thousandsof processors. Frequently the analysis codes produce distributed data and consume a significant portion of the available memory per node. This articlepresents 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.