98 citations · 105 across the 8 of their papers we have counts for
8 papers
Black-Box Statistical Prediction of Lossy Compression Ratios for Scientific Data
Robert Underwood, Julie Bessac, David Krasowska +3
Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and mov…
SIMD Lossy Compression for Scientific Data
Griffin Dube, Jiannan Tian, Sheng Di +3
Modern HPC applications produce increasingly large amounts of data, which limits the performance of current extreme-scale systems. Data reduction techniques, such as lossy compress…
Towards Aggregated Asynchronous Checkpointing
Mikaila J. Gossman, Bogdan Nicolae, Jon C. Calhoun +2
High-Performance Computing (HPC) applications need to checkpoint massive amounts of data at scale. Multi-level asynchronous checkpoint runtimes like VELOC (Very Low Overhead Checkp…
Exploring Lossy Compressibility through Statistical Correlations of Scientific Datasets
David Krasowska, Julie Bessac, Robert Underwood +3
Lossy compression plays a growing role in scientific simulations where the cost of storing their output data can span terabytes. Using error bounded lossy compression reduces the a…
SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors
Xin Liang, Kai Zhao, Sheng Di +9
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 spac…
cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data
Jiannan Tian, Sheng Di, Kai Zhao +8
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 h…