Scientific simulations on high-performance computing (HPC) systems can generate large
amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the …

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC
applications because it not only significantly reduces storage overhead but also can retain …

Abstract The Joint Laboratory on Extreme-Scale Computing (JLESC) was initiated at the
same time lossy compression for scientific data became an important topic for the scientific …

Error-bounded lossy compression is one of the most effective techniques for reducing
scientific data sizes. However, the traditional trial-and-error approach used to configure …

As supercomputers advance towards exascale capabilities, computational intensity
increases significantly, and the volume of data requiring storage and transmission …

While the memory bandwidth of accelerators such as GPU has significantly improved over
the last decade, the commodity networks such as Ethernet and InfiniBand are lagging in …

Lossy compression is one of the most efficient solutions to reduce storage overhead and
improve I/O performance for HPC applications. However, existing parallel I/O libraries …

As the computation power of supercomputers increases, so does simulation size, which in
turn produces orders-of-magnitude more data. Because generated data often exceed the …

Fully Sharded Data Parallel (FSDP) technology achieves higher performance by scaling out
data-parallel training of Deep Learning (DL) models. It shards the model parameters …

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