NeurLZ: On Systematically Enhancing Lossy Compression Performance for Scientific Data based on Neural Learning with Error Control

Wenqi Jia, Youyuan Liu, Zhewen Hu, Jinzhen Wang, Boyuan Zhang, Wei Niu, Junzhou Huang, Stavros Kalafatis, Sian Jin, Miao Yin
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Abstract

Large-scale scientific simulations generate massive datasets that pose significant challenges for storage and I/O. While traditional lossy compression techniques can improve performance, balancing compression ratio, data quality, and throughput remains difficult. To address this, we propose NeurLZ, a novel cross-field learning-based and error-controlled compression framework for scientific data. By integrating skipping DNN models, cross-field learning, and error control, our framework aims to substantially enhance lossy compression performance. Our contributions are three-fold: (1) We design a lightweight skipping model to provide high-fidelity detail retention, further improving prediction accuracy. (2) We adopt a cross-field learning approach to significantly improve data prediction accuracy, resulting in a substantially improved compression ratio. (3) We develop an error control approach to provide strict error bounds according to user requirements. We evaluated NeurLZ on several real-world HPC application datasets, including Nyx (cosmological simulation), Miranda (large turbulence simulation), and Hurricane (weather simulation). Experiments demonstrate that our framework achieves up to a 90% relative reduction in bit rate under the same data distortion, compared to the best existing approach.
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NeurLZ:基于误差控制的神经学习,系统地提高科学数据的有损压缩性能
大规模科学模拟会产生海量数据集,给存储和 I/O 带来巨大挑战。虽然传统的有损压缩技术可以提高性能,但要在压缩率、数据质量和吞吐量之间取得平衡仍然很困难。为了解决这个问题,我们提出了 NeurLZ,这是一种基于跨领域学习和误差控制的新型科学数据压缩框架。通过整合跳转 DNN 模型、跨场学习和错误控制,我们的框架旨在大幅提高有损压缩性能。我们的贡献有三个方面:(1)我们设计了一个轻量级跳转模型,以提供高保真细节保留,进一步提高预测精度。(2) 我们采用跨场学习方法来显著提高数据预测的准确性,从而大幅提高压缩率。(3) 我们开发了一种误差控制方法,可根据用户要求提供严格的误差界限。我们在多个真实世界的 HPC 应用数据集上评估了 NeurLZ,包括 Nyx(宇宙学模拟)、Miranda(大型湍流模拟)和 Hurricane(天气模拟)。实验证明,与现有的最佳方法相比,我们的框架在相同的数据失真条件下实现了高达 90% 的比特率相对降低。
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