Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage
efficiency for HPC applications generating vast volumes of data. However, their applicability …

Particle data are used across a diverse set of large scale simulations, for example, in
cosmology, molecular dynamics and combustion. At scale these applications generate …

Scientific phenomena are being simulated at ever-increasing resolution and fidelity thanks
to advances in modern supercomputers. These simulations produce a deluge of data …

As our ability to generate large and complex datasets grows, accessing and processing
these massive data collections is increasingly the primary bottleneck in scientific analysis …

Hierarchical data representations have been shown to be effective tools for coping with
large-scale scientific data. Writing hierarchical data on supercomputers, however, is …

Today's massively parallel simulation codes can produce output ranging up to many
terabytes of data. Utilizing this data to support scientific inquiry requires analysis and …

Abstract Graphics Processing Units (GPUs) have proven as a viable technology for a wide
variety of general purpose applications to exploit the massive computing capability and high …

Huge amounts of geospatial rasters, such as remotely sensed imagery and environmental
modeling output, are being generated with increasingly finer spatial, temporal, spectral and …

The increase in computational power of supercomputers is enabling complex scientific
phenomena to be simulated at ever-increasing resolution and fidelity. With these simulations …