LCP: Enhancing Scientific Data Management with Lossy Compression for Particles
Summary: LCP is a lossy compressor for particle datasets to improve HPC data management. LCP-S is an error-bound aware block-wise spatial coder, broadly applicable to particle data; LCP is a hybrid compressor delivering superior quality and speed vs prior particle codecs. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Longtao Zhang (Florida State University)
- 2. Ruoyu Li (Florida State University)
- 3. Congrong Ren (Ohio State University)
- 4. Sheng Di (Argonne National Laboratory; University of Chicago)
- 5. Jinyang Liu (University of Houston)
- 6. Jiajun Huang (University of California Riverside)
- 7. Robert Underwood (Argonne National Laboratory; University of Chicago)
- 8. Pascal Grosset (Los Alamos National Laboratory)
- 9. Dingwen Tao (Indiana University)
- 10. Xin Liang (University of Kentucky)
- 11. Hanqi Guo (Ohio State University)
- 12. Franck Cappello (Argonne National Laboratory; University of Chicago)
- 13. Kai Zhao (Florida State University)
BibTeX Citation
@inproceedings{zhang_sigmod25,
title = {{LCP: Enhancing Scientific Data Management with Lossy Compression for Particles}},
author = {Zhang, Longtao and Li, Ruoyu and Ren, Congrong and Di, Sheng and Liu, Jinyang and Huang, Jiajun and Underwood, Robert and Grosset, Pascal and Tao, Dingwen and Liang, Xin and Guo, Hanqi and Cappello, Franck and Zhao, Kai},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709700},
url = {https://dl.acm.org/doi/10.1145/3709700},
year = {2025}
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