Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

The term “in situ processing” has evolved over the last decade to mean both a specific
strategy for visualizing and analyzing data and an umbrella term for a processing paradigm …

We analyze the opportunities for in-transit visualization to provide cost savings compared to
in-line visualization. We begin by develo** a cost model that includes factors related to …

High-Performance Computing (HPC) systems provide input/output (IO) performance growing
relatively slowly compared to peak computational performance and have limited storage …

The visualization of results while the simulation is running is increasingly common in
extreme scale computing environments. We present a novel approach for in situ generation …

One key challenge when doing in situ processing is the investment required to add code to
numerical simulations needed to take advantage of in situ processing. Such instrumentation …

One of the primary challenges facing scientists is extracting understanding from the large
amounts of data produced by simulations, experiments, and observational facilities. The use …

Triggers are an emerging strategy for optimizing execution time for in situ analysis. However,
their performance characteristics are complex, making it difficult to decide if a particular …

This short paper considers time-to-solution for two in situ visualization paradigms: in-line
and in-transit. It is a follow-on work to two previous studies. The first study [10] considered …

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one
set of ranks M, while a data consumer, such as a parallel visualization application, runs on a …