High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation
Summary: HPEZ (QoZ 2.0): high-performance error-bounded lossy compression for scientific data with auto-tuned multi-component interpolation. Interpolation advances: re-ordering, multi-dimensional interpolation, natural cubic splines; plus block-wise auto-tuning, dynamic dimension freezing, and Lorenzo tuning yield up to 140% higher compression ratio, 360% PSNR, and ~40% faster transfers. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jinyang Liu
- 2. Sheng Di
- 3. Kai Zhao
- 4. Xin Liang
- 5. Sian Jin
- 6. Zizhe Jian
- 7. Jiajun Huang
- 8. Shixun Wu
- 9. Zizhong Chen
- 10. Franck Cappello
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,622 | QPET: A Versatile and Portable Quantity-of-Interest-Preservation Framework for Error-Bounded Lossy Compression | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 211 | Gorilla: A Fast, Scalable, In-Memory Time Series Database | 2015 | VLDB | 0.0003401421 |
| 2,274 | ModelarDB: Modular Model-Based Time Series Management with Spark and Cassandra | 2018 | VLDB | 9.1432785e-05 |
| 3,321 | Trajectory Simplification: An Experimental Study and Quality Analysis | 2018 | VLDB | 7.2212455e-05 |
| 9,452 | Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data | 2023 | VLDB | 4.3363262e-05 |
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