Author:
Krasowska David,Bessac Julie,Underwood Robert,Calhoun Jon C.,Di Sheng,Cappello Franck
Funder
National Science Foundation
DOE's Advanced Scientific Research Office (ASCR)
Cited by
7 articles.
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1. LibPressio-Predict: Flexible and Fast Infrastructure For Inferring Compression Performance;Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis;2023-11-12
2. A Lightweight, Effective Compressibility Estimation Method for Error-bounded Lossy Compression;2023 IEEE International Conference on Cluster Computing (CLUSTER);2023-10-31
3. Black-box statistical prediction of lossy compression ratios for scientific data;The International Journal of High Performance Computing Applications;2023-06-14
4. Hierarchical Residual Encoding for Multiresolution Time Series Compression;Proceedings of the ACM on Management of Data;2023-05-26
5. Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”;Journal of Agricultural, Biological and Environmental Statistics;2023-05-11