Use cases of lossy compression for floating-point data in scientific data sets
Architectural and technological trends of systems used for scientific computing call for a
significant reduction of scientific data sets that are composed mainly of floating-point data …
significant reduction of scientific data sets that are composed mainly of floating-point data …
Exascale applications: skin in the game
As noted in Wikipedia, skin in the game refers to having 'incurred risk by being involved in
achieving a goal', where 'skin is a synecdoche for the person involved, and game is the …
achieving a goal', where 'skin is a synecdoche for the person involved, and game is the …
Error-controlled lossy compression optimized for high compression ratios of scientific datasets
Today's scientific simulations require a significant reduction of the data size because of
extremely large volumes of data they produce and the limitation of storage bandwidth and …
extremely large volumes of data they produce and the limitation of storage bandwidth and …
SZ3: A modular framework for composing prediction-based error-bounded lossy compressors
Today's scientific simulations require a significant reduction of data volume because of
extremely large amounts of data they produce and the limited I/O bandwidth and storage …
extremely large amounts of data they produce and the limited I/O bandwidth and storage …
High-ratio lossy compression: Exploring the autoencoder to compress scientific data
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 …
amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the …
Cusz: An efficient gpu-based error-bounded lossy compression framework for scientific data
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 …
applications because it not only significantly reduces storage overhead but also can retain …
Understanding and modeling lossy compression schemes on HPC scientific data
Scientific simulations generate large amounts of floating-point data, which are often not very
compressible using the traditional reduction schemes, such as deduplication or lossless …
compressible using the traditional reduction schemes, such as deduplication or lossless …
Significantly improving lossy compression for HPC datasets with second-order prediction and parameter optimization
Today's extreme-scale high-performance computing (HPC) applications are producing
volumes of data too large to save or transfer because of limited storage space and I/O …
volumes of data too large to save or transfer because of limited storage space and I/O …
Multilevel techniques for compression and reduction of scientific data---The multivariate case
We develop a technique for multigrid adaptive reduction of data (MGARD). Special attention
is given to the case of tensor product grids, where our approach permits the use of …
is given to the case of tensor product grids, where our approach permits the use of …
Error-bounded learned scientific data compression with preservation of derived quantities
Scientific applications continue to grow and produce extremely large amounts of data, which
require efficient compression algorithms for long-term storage. Compression errors in …
require efficient compression algorithms for long-term storage. Compression errors in …