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 …

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

Today's scientific high-performance computing applications and advanced instruments are
producing vast volumes of data across a wide range of domains, which impose a serious …

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

Stream processing prevails and SQL query on streams has become one of the most popular
application scenarios. For example, in 2021, the global number of active IoT endpoints …

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 …

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

Error-bounded lossy compression is a critical technique for significantly reducing scientific
data volumes. Compared to CPU-based compressors, GPU-based compressors exhibit …

Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage
efficiency for HPC applications generating vast volumes of data. However, their applicability …

Today's graphics processing unit (GPU) applications produce vast volumes of data, which
are challenging to store and transfer efficiently. Thus, data compression is becoming a …