# Lessons Learned on the Path to Guaranteeing the Error Bound in Lossy Quantizers

arXiv: https://arxiv.org/abs/2407.15037 · HTML: https://mlsanity.com/paper/2407.15037/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors](https://mlsanity.com/paper/2111.02925/) (2021)
- [cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data](https://mlsanity.com/paper/2007.09625/) (2020)
- [SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors](https://mlsanity.com/paper/2101.03201/) (2021)
- [MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction](https://mlsanity.com/paper/2010.05872/) (2020)
- [Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs](https://mlsanity.com/paper/2007.04457/) (2020)
- [FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs](https://mlsanity.com/paper/2304.12557/) (2023)
- [Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs](https://mlsanity.com/paper/2105.12912/) (2021)

## Abstract (arXiv)

Rapidly increasing data sizes in scientific computing are the driving force behind the need for lossy compression. The main drawback of lossy data compression is the introduction of error. This paper explains why many error-bounded compressors occasionally violate the error bound and presents the solutions we use in LC, a CPU/GPU compatible lossy compression framework, to guarantee the error bound for all supported types of quantizers. We show that our solutions maintain high compression ratios and cause no appreciable change in throughput.