Multi-resolution methods such as Adaptive Mesh Refinement (AMR) can enhance storage efficiency for HPC applications generating vast volumes of data. However, their applicability is limited and cannot be universally deployed across all applications. Furthermore, integrating lossy compression with multi-resolution techniques to further boost storage efficiency encounters significant barriers. To this end, we introduce an innovative workflow that facilitates high-quality multi-resolution data compression for both uniform and AMR simulations. Initially, to extend the usability of multi-resolution techniques, our workflow employs a compression-oriented Region of Interest (ROI) extraction method, transforming uniform data into a multi-resolution format. Subsequently, to bridge the gap between multi-resolution techniques and lossy compressors, we optimize three distinct compressors, ensuring their optimal performance on multi-resolution data. Lastly, we incorporate an advanced uncertainty visualization method into our workflow to understand the potential impacts of lossy compression. Experimental evaluation demonstrates that our workflow achieves significant compression quality improvements.

翻译：诸如自适应网格细化（AMR）等多分辨率方法能够提升生成海量数据的高性能计算应用的存储效率。然而，其适用性有限，无法在所有应用中普遍部署。此外，将有损压缩与多分辨率技术相结合以进一步提升存储效率面临着重大障碍。为此，我们提出了一种创新的工作流程，该流程能够为均匀网格和AMR模拟实现高质量的多分辨率数据压缩。首先，为了扩展多分辨率技术的可用性，我们的工作流程采用了一种面向压缩的感兴趣区域提取方法，将均匀数据转换为多分辨率格式。随后，为了弥合多分辨率技术与有损压缩器之间的差距，我们优化了三种不同的压缩器，确保其在多分辨率数据上达到最佳性能。最后，我们在工作流程中集成了一种先进的不确定性可视化方法，以理解有损压缩的潜在影响。实验评估表明，我们的工作流程实现了显著的压缩质量提升。