In this survey, we discuss the challenges of executing scientific workflows as well as existing
Machine Learning (ML) techniques to alleviate those challenges. We provide the context …

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 …

We propose a new visualization method for massive supercomputer simulations. The key
idea is to scatter multiple omnidirectional cameras to record the simulation via in situ …

One of the primary challenges facing scientists is extracting understanding from the large
amounts of data produced by simulations, experiments, and observational facilities. The use …

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

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 …

We introduce a visual analysis system with GPU acceleration techniques for large sets of
trajectories from complex dynamical systems. The approach is based on an interactive …

Emerging high performance computing (HPC) systems are expected to be deployed with an
unprecedented level of complexity due to a deep system memory and storage hierarchy …

Extreme-scale systems for compute-and data-centric science are pushing the boundaries of
system architectures in order to achieve ambitious goals in terms of computing capability …