Scientific data analysis typically involves reading massive amounts of data that was
generated by simulations, experiments, and observations. Performance of reading such …

We present H2DP, a holistic heterogeneity-aware data placement scheme for hybrid parallel
I/O systems, which consist of HDD servers and SSD servers. Most of the existing approaches …

While large-scale scientific experiments and simulations produce massive amounts of data,
a small fraction of data contains useful information. Efficient querying on such volume of data …

Applications and middleware services, such as data placement engines, I/O scheduling, and
prefetching engines, require low-latency access to telemetry data in order to make optimal …

Understanding large amounts of spatiotemporal data from particle-based simulations, such
as molecular dynamics, often relies on the computation and analysis of aggregate …

Scientific data analytics in high-performance computing environments has been evolving
along with the advancement of computing capabilities. With the onset of exascale …

As the diversity of big data applications increases, their requirements diverge and often
conflict with one other. Managing this diversity in any supercomputer or data center is a …

Scientific experiments and simulations produce mountains of data in file formats, such as
HDF5, NetCDF, and FITS. Often, a relatively small amount of data holds the key to new …

Parallel I/O is a critical technique for moving data between compute and storage subsystems
of supercomputers. With massive amounts of data produced or consumed by compute …

Upcoming exascale high performance computing (HPC) systems are expected to comprise
multi-tier storage hierarchy, and thus will necessitate innovative storage and I/O …