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We present the results of a series of experiments studying how visualization software scales to massive data sets. Although several paradigms exist for processing large data, we focus on pure parallelism,the dominant approach for production software. These experiments utilized multiple visualization algorithms and were run on multiple architectures. Two types of experiments were performed.For the first, we examined performance at massive scale:16,000 or more cores and one trillion or more cells. For the second, we studied weak scaling performance. These experiments were performed on the largest data set sizes published to date in visualization literature, and the findings on scaling characteristics and bottlenecks contribute to understanding of how pure parallelism will perform at high levels of concurrency and with very large data sets.