The fast growth of computational power and scales of modern super-computing systems have raised great challenges for the management of exascale scientific data. To maintain the usability of scientific data, error-bound lossy compression is proposed and developed as an essential technique for the size reduction of scientific data with constrained data distortion. Among the diverse datasets generated by various scientific simulations, certain datasets cannot be effectively compressed by existing error-bounded lossy compressors with traditional techniques. The recent success of Artificial Intelligence has inspired several researchers to integrate neural networks into error-bounded lossy compressors. However, those works still suffer from limited compression ratios and/or extremely low efficiencies. To address those issues and improve the compression on the hard-to-compress datasets, in this paper, we propose SRN-SZ, which is a deep learning-based scientific error-bounded lossy compressor leveraging the hierarchical data grid expansion paradigm implemented by super-resolution neural networks. SRN-SZ applies the most advanced super-resolution network HAT for its compression, which is free of time-costing per-data training. In experiments compared with various state-of-the-art compressors, SRN-SZ achieves up to 75% compression ratio improvements under the same error bound and up to 80% compression ratio improvements under the same PSNR than the second-best compressor.

翻译：现代超级计算系统的计算能力和规模快速增长，给百亿亿级科学数据的管理带来了巨大挑战。为保持科学数据的可用性，误差有界有损压缩被提出并发展为一种在约束数据失真的前提下缩减科学数据规模的关键技术。在各类科学模拟生成的多维数据集中，部分数据集无法被现有采用传统技术的误差有界有损压缩器有效压缩。人工智能领域的近期成功促使研究者将神经网络集成到误差有界有损压缩器中。然而，这些工作仍存在压缩率有限或效率极低等问题。为解决上述难题并提升对难压缩数据集的压缩效果，本文提出SRN-SZ——一种基于深度学习的科学误差有界有损压缩器，其利用超分辨率神经网络实现分级数据网格扩展范式。SRN-SZ采用当前最先进的超分辨率网络HAT进行压缩，该方法无需耗时的逐数据训练。在与多种最先进压缩器的对比实验中，相同误差界条件下，SRN-SZ的压缩率提升最高达75%；在相同峰值信噪比条件下，相较于次优压缩器，其压缩率提升最高达80%。