# FZ-GPU: A Fast and High-Ratio Lossy Compressor for Scientific Computing Applications on GPUs

arXiv: https://arxiv.org/abs/2304.12557 · HTML: https://mlsanity.com/paper/2304.12557/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 5 of 5)

- [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)
- [Lessons Learned on the Path to Guaranteeing the Error Bound in Lossy Quantizers](https://mlsanity.com/paper/2407.15037/) (2024)
- [Mitigating Artifacts in Pre-quantization Based Scientific Data Compressors with Quantization-aware Interpolation](https://mlsanity.com/paper/2602.20097/) (2026)

## Abstract (arXiv)

Today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, data compression is becoming a critical technique to mitigate the storage burden and data-movement cost. However, existing lossy compressors for scientific data cannot achieve a high compression ratio and throughput simultaneously, hindering their adoption in many applications requiring fast compression, such as in-memory compression. To this end, in this work, we develop a fast and high-ratio error-bounded lossy compressor on GPUs for scientific data (called FZ-GPU). Specifically, we first design a new compression pipeline that consists of fully parallelized quantization, bitshuffle, and our newly designed fast encoding. Then, we propose a series of deep architectural optimizations for each kernel in the pipeline to take full advantage of CUDA architectures. We propose a warp-level optimization to avoid data conflicts for bit-wise operations in bitshuffle, maximize shared memory utilization, and eliminate unnecessary data movements by fusing different compression kernels. Finally, we evaluate FZ-GPU on two NVIDIA GPUs (i.e., A100 and RTX A4000) using six representative scientific datasets from SDRBench. Results on the A100 GPU show that FZ-GPU achieves an average speedup of 4.2X over cuSZ and an average speedup of 37.0X over a multi-threaded CPU implementation of our algorithm under the same error bound. FZ-GPU also achieves an average speedup of 2.3X and an average compression ratio improvement of 2.0X over cuZFP under the same data distortion.