2 citations · 2 across the 3 of their papers we have counts for
3 papers
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.DC2023
FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications
Grant Wilkins, Sheng Di, Jon C. Calhoun +5
With the promise of federated learning (FL) to allow for geographically-distributed and highly personalized services, the efficient exchange of model updates between clients and se…