The applications being developed within the US Exascale Computing Project (ECP) to run
on imminent Exascale computers will generate scientific results with unprecedented fidelity …

When working with a large dataset, a relatively small fraction of data records are of interest
in each analysis operation. For example, while examining a billion-particle dataset from an …

With the convergence of high-performance computing (HPC), big data and artificial
intelligence (AI), the HPC community is pushing for “triple use” systems to expedite scientific …

Indexing technique has become an efficient tool to enable scientists to directly access the
most relevant data records. But, the time and space requirements of building and storing …

Scientific exploration and discovery heavily rely on increasing datasets and strong
supercomputing power. Surging data pose massive data management challenges in …

In times of continuously growing data sizes, performing insightful analysis is increasingly
difficult. I/O libraries such as NetCDF and ADIOS2 offer options to manage additional …

As data analysis scenarios keep increasing on high‐performance computing systems, the
ability to select a small fraction of data from a large volume of scientific data sets is vital to …

Emerging scientific computing generates massive amounts of scientific data by relying on
high-performance computer systems, challenging data management and analysis. State-of …

While the ability to access a small fraction of data records from a large volume of scientific
datasets is vital to accelerate scientific discovery, existing parallel file systems face serious …