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 …

Although supercomputers are becoming increasingly powerful, their components have thus
far not scaled proportionately. Compute power is growing enormously and is enabling finely …

CoDDA (C opula-based D istribution D riven A nalysis) is a flexible framework for large-
scale multivariate datasets. A common strategy to deal with large-scale scientific simulation …

The study of multiphase flow is essential for designing chemical reactors such as fluidized
bed reactors (FBR), as a detailed understanding of hydrodynamics is critical for optimizing …

The VTK-m software library enables scientific visualization on exascale-class
supercomputers. Exascale machines are particularly challenging for software development …

The development of usable visualization solutions is essential for ensuring both their
adoption and effectiveness. User-centered design principles, which involve users …

Considering that simulations of turbulent combustion are computationally expensive, this
chapter takes a decidedly different perspective, that of high-performance computing (HPC) …

With exascale supercomputers on the horizon, data-driven in situ data reduction is a very
important topic that potentially enables post hoc data visualization, reconstruction, and …

Scientists nowadays use data sets generated from large-scale scientific computational
simulations to understand the intricate details of various physical phenomena. These …

Despite the popularity of in-situ analytics in scientific computing, there is only limited work to
date on in-situ analytics for simulations running on GPUs. Notably, two unaddressed …