2 citations · 3 across the 6 of their papers we have counts for
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cs.DC2024
To Compress or Not To Compress: Energy Trade-Offs and Benefits of Lossy Compressed I/O
Grant Wilkins, Sheng Di, Jon C. Calhoun +2
Modern scientific simulations generate massive volumes of data, creating significant challenges for I/O and storage systems. Error-bounded lossy compression (EBLC) offers a solutio…
cs.DC2024★ 2 cited
A Survey on Error-Bounded Lossy Compression for Scientific Datasets
Sheng Di, Jinyang Liu, Kai Zhao +23
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error…
cs.LG2024★ 1 cited
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…