Recent studies have indicated that foundation models, such as BERT and GPT, excel at
adapting to various downstream tasks. This adaptability has made them a dominant force in …

The next generation of supercomputers will break the exascale barrier. Soon we will have
systems capable of at least one quintillion (billion billion) floating-point operations per …

Extreme scale scientific simulations are leading a charge to exascale computation, and data
analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O …

Visualization and analysis tools, techniques, and algorithms have undergone a rapid
evolution in recent decades to accommodate explosive growth in data size and complexity …

The considerable interest in the high performance computing (HPC) community regarding
analyzing and visualization data without first writing to disk, ie, in situ processing, is due to …

There is a widening gap between compute performance and the ability to store computation
results. Complex scientific codes are the most affected since they must save massive files …

Automation of the execution of computational tasks is at the heart of improving scientific
productivity. Over the last years, scientific workflows have been established as an important …

We propose InSituNet, a deep learning based surrogate model to support parameter space
exploration for ensemble simulations that are visualized in situ. In situ visualization …

As scientific supercomputing moves toward petascale and exascale levels, in situ
visualization stands out as a scalable way for scientists to view the data their simulations …

For extreme-scale high-performance computing systems, system-wide power consumption
has been identified as one of the key constraints moving forward, where DRAM main …