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Particle tracing for streamline and pathline generation is a common method of visualizing vector ?elds in scienti?c data, but it is dif?cult to parallelize ef?ciently because of demanding and widely varying computational and communication loads. In this paper we scale parallel particle tracing for visualizing steady and unsteady ?ow ?elds well beyond previously published results. We con?gure the 4D domain decomposition into spatial and temporal blocks that combine in-core and out-of-core execution in a ?exible way that favors faster run time or smaller memory. We also compare static and dynamic partitioning approaches. Strong and weak scaling curves are presented for tests conducted on an IBM Blue Gene/P machine at up to 32 K processes using a parallel ?ow visualization library that we are developing. Datasets are derived from computational ?uid dynamics simulations of thermal hydraulics, liquid mixing, and combustion.
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