# cuSZ: An Efficient GPU-Based Error-Bounded Lossy Compression Framework for Scientific Data

arXiv: https://arxiv.org/abs/2007.09625 · HTML: https://mlsanity.com/paper/2007.09625/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Exploring Autoencoder-based Error-bounded Compression for Scientific Data](https://mlsanity.com/paper/2105.11730/) (2021)
- [COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression](https://mlsanity.com/paper/2111.09562/) (2021)
- [MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring](https://mlsanity.com/paper/2401.05994/) (2024)
- [KVComp: A High-Performance, LLM-Aware, Lossy Compression Framework for KV Cache](https://mlsanity.com/paper/2509.00579/) (2025)
- [SRN-SZ: Deep Leaning-Based Scientific Error-bounded Lossy Compression with Super-resolution Neural Networks](https://mlsanity.com/paper/2309.04037/) (2023)
- [Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression](https://mlsanity.com/paper/2407.04272/) (2024)
- [Lossy compression of statistical data using quantum annealer](https://mlsanity.com/paper/2110.02142/) (2021)
- [Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data](https://mlsanity.com/paper/2310.15026/) (2023)
- [Machine Learning Techniques for Data Reduction of Climate Applications](https://mlsanity.com/paper/2405.00879/) (2024)
- [Accelerating Large Language Model Training with Hybrid GPU-based Compression](https://mlsanity.com/paper/2409.02423/) (2024)
- [SIMD Lossy Compression for Scientific Data](https://mlsanity.com/paper/2201.04614/) (2022)
- [What Operations can be Performed Directly on Compressed Arrays, and with What Error?](https://mlsanity.com/paper/2406.11209/) (2024)
- [Lessons Learned on the Path to Guaranteeing the Error Bound in Lossy Quantizers](https://mlsanity.com/paper/2407.15037/) (2024)
- [Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications](https://mlsanity.com/paper/2409.18295/) (2024)
- [Variable Rate Neural Compression for Sparse Detector Data](https://mlsanity.com/paper/2411.11942/) (2024)
- [Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation](https://mlsanity.com/paper/2602.20097/) (2026)
- [NCCLZ: Compression-Enabled GPU Collectives with Decoupled Quantization and Entropy Coding](https://mlsanity.com/paper/2605.12396/) (2026)
- [AlignPred: Error-Controlled KV Cache Compression via Alignment and Predictive Coding](https://mlsanity.com/paper/2610.17970/) (2026)

## Abstract (arXiv)

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity for postanalysis. Because supercomputers and HPC applications are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existing state-of-the-art GPU-based lossy compressors suffer from either low compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version, cuSZ, for one of the best error-bounded lossy compressors-SZ. To the best of our knowledge, cuSZ is the first error-bounded lossy compressor on GPUs for scientific data. Our contributions are fourfold. (1) We propose a dual-quantization scheme to entirely remove the data dependency in the prediction step of SZ such that this step can be performed very efficiently on GPUs. (2) We develop an efficient customized Huffman coding for the SZ compressor on GPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4) We evaluate our cuSZ on five real-world HPC application datasets from the Scientific Data Reduction Benchmarks and compare it with other state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughput by up to 370.1x and 13.1x, respectively, over the production version running on single and multiple CPU cores, respectively, while getting the same quality of reconstructed data. It also improves the compression ratio by up to 3.48x on the tested data compared with another state-of-the-art GPU supported lossy compressor.