Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. Compared to CPU-based compressors, GPU-based compressors exhibit substantially higher throughputs, fitting better for today's HPC applications. However, the critical limitations of existing GPU-based compressors are their low compression ratios and qualities, severely restricting their applicability. To overcome these, we introduce a new GPU-based error-bounded scientific lossy compressor named cuSZ-$i$, with the following contributions: (1) A novel GPU-optimized interpolation-based prediction method significantly improves the compression ratio and decompression data quality. (2) The Huffman encoding module in cuSZ-$i$ is optimized for better efficiency. (3) cuSZ-$i$ is the first to integrate the NVIDIA Bitcomp-lossless as an additional compression-ratio-enhancing module. Evaluations show that cuSZ-$i$ significantly outperforms other latest GPU-based lossy compressors in compression ratio under the same error bound (hence, the desired quality), showcasing a 476% advantage over the second-best. This leads to cuSZ-$i$'s optimized performance in several real-world use cases.

翻译：误差有界有损压缩是显著减少科学数据体量的关键技术。与基于CPU的压缩器相比，基于GPU的压缩器展现出显著更高的吞吐量，更适配当今的高性能计算应用。然而，现有基于GPU的压缩器存在压缩比与数据质量较低的关键局限，严重限制了其适用性。为克服这些局限，我们提出了一种名为cuSZ-$i$的新型基于GPU的误差有界科学数据有损压缩器，其主要贡献如下：(1) 一种新颖的基于GPU优化的插值预测方法，显著提升了压缩比与解压数据质量。(2) cuSZ-$i$中的霍夫曼编码模块经过优化以实现更高效率。(3) cuSZ-$i$首次集成了NVIDIA Bitcomp无损压缩模块作为额外的压缩比增强组件。评估表明，在相同误差界限（即同等期望质量）下，cuSZ-$i$的压缩比显著优于其他最新的基于GPU的有损压缩器，相较于次优方案展现出476%的优势。这使得cuSZ-$i$在多个实际应用场景中实现了优化性能。