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 …

One of the core issues across computer and computational science today is adapting to,
managing, and learning from the influx of" Big Data". In the commercial space, this problem …

Scientific simulations on high performance computing (HPC) platforms generate large
quantities of data. To bridge the widening gap between compute and I/O, and enable data to …

As scientific workflows increasingly use extreme-scale resources, the imbalance between
higher computational capabilities, generated data volumes, and available I/O bandwidth is …

Scientific visualization for exascale computing is very likely to require in situ processing.
Traditional simulation checkpointing and post hoc visualization will likely be unsustainable …

As we continue toward exascale, scientific data volume is continuing to scale and becoming
more burdensome to manage. In this paper, we lay out opportunities to enhance state of the …

With this work, we explore the feasibility of using in situ data binning techniques to achieve
significant data reductions for particle data, and study the associated errors for several post …

In situ visualization is increasingly necessary to address I/O limitations on supercomputers.
With the increasing heterogeneity of supercomputer design, efficient and cost effective use …

In-situ and in-transit processing alleviate the gap between the computing and I/O capabilities
by scheduling data analytics close to the data source. Hybrid in-situ processing splits data …

Scientific simulations on high performance computing (HPC) platforms generate large
quantities of data. To bridge the widening gap between compute and I/O, and enable data to …