Abstract-Effective large-scale data visualization remains a significant and 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 paper presents an architectural approach to handling these visualization problems based on mixed dataset topology parallel data streaming. This enables visualizations on a parallel cluster that would normally require more storage/memory than is available while at the same time achieving high code reuse. Results from a variety of hardware and visualization configurations are discussed with data sizes ranging near to a petabyte.
Ahrens et al. REF presented an architectural approach to handle large-scale visualization problems with parallel data streaming.
Large-scale data visualization using parallel data streaming
IEEE Computer Graphics and Applications
IEEE Computer Graphics and Applications
Computer Science