# High-performance Effective Scientific Error-bounded Lossy Compression with Auto-tuned Multi-component Interpolation

arXiv: https://arxiv.org/abs/2311.12133 · HTML: https://mlsanity.com/paper/2311.12133/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2023.

## Built on by (most cited first, 9 of 9)

- [GWLZ: A Group-wise Learning-based Lossy Compression Framework for Scientific Data](https://mlsanity.com/paper/2404.13470/) (2024)
- [NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression](https://mlsanity.com/paper/2409.05785/) (2024)
- [FT K-means: A High-Performance K-means on GPU with Fault Tolerance](https://mlsanity.com/paper/2408.01391/) (2024)
- [GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders](https://mlsanity.com/paper/2505.06316/) (2025)
- [LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)](https://mlsanity.com/paper/2510.23632/) (2025)
- [DeepCQ: General-Purpose Deep-Surrogate Framework for Lossy Compression Quality Prediction](https://mlsanity.com/paper/2512.21433/) (2025)
- [ImplicitTerrainV2: Wavelet-Guided Spatially Adaptive Neural Terrain Representation](https://mlsanity.com/paper/2605.22556/) (2026)
- [Can Deep Neural Networks Improve Compression of Very Large Scientific Data?](https://mlsanity.com/paper/2606.14353/) (2026)
- [Cadence: Error-Bounded Lossy Compression of Demand Time Series with a Time-Series Foundation Model](https://mlsanity.com/paper/2609.06008/) (2026)

## Abstract (arXiv)

Error-bounded lossy compression has been identified as a promising solution for significantly reducing scientific data volumes upon users' requirements on data distortion. For the existing scientific error-bounded lossy compressors, some of them (such as SPERR and FAZ) can reach fairly high compression ratios and some others (such as SZx, SZ, and ZFP) feature high compression speeds, but they rarely exhibit both high ratio and high speed meanwhile. In this paper, we propose HPEZ with newly-designed interpolations and quality-metric-driven auto-tuning, which features significantly improved compression quality upon the existing high-performance compressors, meanwhile being exceedingly faster than high-ratio compressors. The key contributions lie in the following points: (1) We develop a series of advanced techniques such as interpolation re-ordering, multi-dimensional interpolation, and natural cubic splines to significantly improve compression qualities with interpolation-based data prediction. (2) The auto-tuning module in HPEZ has been carefully designed with novel strategies, including but not limited to block-wise interpolation tuning, dynamic dimension freezing, and Lorenzo tuning. (3) We thoroughly evaluate HPEZ compared with many other compressors on six real-world scientific datasets. Experiments show that HPEZ outperforms other high-performance error-bounded lossy compressors in compression ratio by up to 140% under the same error bound, and by up to 360% under the same PSNR. In parallel data transfer experiments on the distributed database, HPEZ achieves a significant performance gain with up to 40% time cost reduction over the second-best compressor.