Extremely high data rates at modern synchrotron and X-ray free-electron laser light source
beamlines motivate the use of machine learning methods for data reduction, feature …

The increasing volume and velocity of science data necessitate the frequent movement of
enormous data volumes as part of routine research activities. As a result, limited wide-area …

High Performance Computing (HPC) is an important method for scientific discovery via large-
scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers …

Today's exa-scale scientific applications or advanced instruments are producing vast
volumes of data, which need to be shared/transferred through the network/devices with …

Modern scientific instruments, such as detectors at synchrotron light sources, generate data
at such high rates that online processing is needed for data reduction, feature detection …

File transfers in high-speed networks require network and I/O parallelism to reach high
speeds, however, creating arbitrarily large numbers of I/O and network threads overwhelms …

Many extreme-scale applications require the movement of large quantities of data to, from,
and among leadership computing facilities, as well as other scientific facilities and the home …

Parallelism is key to efficiently utilizing high-speed research networks when transferring
large volumes of data. However, the monolithic design of existing transfer applications …

Modern science and engineering computing environments often feature storage systems of
different types, from parallel file systems in high-performance computing centers to object …

WAN bandwidth is never too broad---and the speed of light stubbornly constant. These two
fundamental constraints force globally-distributed systems to carefully replicate data close to …