Modern scientific applications and supercomputing systems are generating large amounts of
data in various fields, leading to critical challenges in data storage footprints and …

Error-bounded lossy compression has been effective in significantly reducing the data
storage/transfer burden while preserving the reconstructed data fidelity very well. Many error …

Today's large-scale scientific applications running on high-performance computing (HPC)
systems generate vast data volumes. Thus, data compression is becoming a critical …

Lossy compression and asynchronous I/O are two of the most effective solutions for reducing
storage overhead and enhancing I/O performance in large-scale high-performance …

In scientific simulations, observations, and experiments, the transfer of data to and from disk
and across networks has become a major bottleneck for data analysis and visualization …

Existing GPU lossy compressors suffer from expensive data movement overheads,
inefficient memory access patterns, and high synchronization latency, resulting in limited …

As the growth of data sizes continues to outpace computational resources, there is a
pressing need for data reduction techniques that can significantly reduce the amount of data …

More and more HPC applications require fast and effective compression techniques to
handle large volumes of data in storage and transmission. Not only do these applications …

Real-time data collection and analysis in large experimental facilities present a great
challenge across multiple domains, including high energy physics, nuclear physics, and …

The increase of computer processing speed is significantly outpacing improvements in
network and storage bandwidth, leading to the big data challenge in modern science, where …