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

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

Large-scale scientific simulations generate massive datasets that pose significant
challenges for storage and I/O. While traditional lossy compression techniques can improve …

Block-coder style data compression schemes, such as JPEG, ZFP and SPERR, rely on de-
correlating transformations in order to efficiently store data with entropy encoding schemes …

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 …

Data reduction is now frequently used by simulations to reduce the amount of data that
needs to be stored. Consequently, several error-bound lossy data reduction techniques …

As high-performance computing systems continue to advance, the gap between computing
performance and I/O capabilities is widening. This bottleneck limits the storage capabilities …

High fidelity unsteady computational fluid dynamics (CFD) simulations can generate
extremely large datasets. It is often desirable to save as much of this data as possible …

Vector embeddings search (VES) is an important component of applications such as pattern
recognition, recommendation systems, and retrieval-augmented generation. This search …