The single-batch machine, commonly found in industrial manufacturing, can concurrently
process a group of jobs in variable-speed batches, leading to fluctuating levels of both …

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

We explore an error‐bounded lossy compression approach for reducing scientific data
associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high …

Today's scientific simulations require significant data volume reduction because of the
enormous amounts of data produced and the limited I/O bandwidth and storage space. Error …

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

With the scaling-up of high-performance computing (HPC) systems, the resilience has
become an important challenge. As a widely used resilience technique for HPC systems …

Today's scientific simulations, for example in the high-performance exascale sector, produce
huge amounts of data. Due to limited I/O bandwidth and available storage space, there is the …

Error-bounded lossy compression has been a critical technique to significantly reduce the
sheer amounts of simulation datasets for high-performance computing (HPC) scientific …

Traditional scientific visualization pipelines transfer entire data arrays from storage to client
nodes for processing into displayable graphics objects. However, this full data transfer is …

Lossy compression is one of the most effective methods for reducing the size of scientific
data containing multiple data fields. It reduces information density through prediction or …