26 citations · 55 across the 11 of their papers we have counts for
11 papers
Understanding The Effectiveness of Lossy Compression in Machine Learning Training Sets
Robert Underwood, Jon C. Calhoun, Sheng Di +1
Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of flo…
Dynamic Quality Metric Oriented Error-bounded Lossy Compression for Scientific Datasets
Jinyang Liu, Sheng Di, Kai Zhao +3
With the ever-increasing execution scale of high performance computing (HPC) applications, vast amounts of data are being produced by scientific research every day. Error-bounded l…
SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks
Jinyang Liu, Sheng Di, Sian Jin +4
The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usa…
AMRIC: A Novel In Situ Lossy Compression Framework for Efficient I/O in Adaptive Mesh Refinement Applications
Daoce Wang, Jesus Pulido, Pascal Grosset +11
As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage and transmission experiences expo…
Optimizing Scientific Data Transfer on Globus with Error-bounded Lossy Compression
Yuanjian Liu, Sheng Di, Kyle Chard +2
The increasing volume and velocity of science data necessitate the frequent movement of enormous data volumes as part of routine research activities. As a result, limited wide-area…
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…