Analysis of large-scale simulation output is a core element of scientific inquiry, but analysis
queries may experience significant I/O overhead when the data is not structured for efficient …

Growing evidence in the scientific computing community indicates that parallel file systems
are not sufficient for all HPC storage workloads. This realization has motivated extensive …

Complex storage stacks providing data compression, indexing, and analytics help leverage
the massive amounts of data generated today to derive insights. It is challenging to perform …

With the emergence of new computing paradigms (eg, cloud and edge computing, big data,
Internet of Things (IoT), deep learning, etc.) and new storage hardware (eg, non-volatile …

Different applications have different access characteristics and various performance
requirements. Thus, the shared cloud object store entails providing tenant-specific policies …

With the emergence of new computing paradigms (eg, cloud and edge computing, big data,
Internet of Things (IoT), deep learning, etc.) and new storage hardware (eg, non-volatile …

A cloud object store entails serving workloads of multi tenants. Different applications have
different access characteristics and various performance requirements. Thus, it is necessary …

Scientific data sets have grown rapidly in recent years, outpacing the growth in memory and
network bandwidths. This I/O bottleneck has made it increasingly difficult for scientists to …

We are approaching a point in time when it will be infeasible to catalog and query data after
it has been generated. This trend has fueled research on in-situ data processing (ie …

With the emergence of new computing paradigms (eg, cloud and edge computing, big data,
Internet of Things (IoT), deep learning, etc.) and new storage hardware (eg, non-volatile …