A High-Quality Workflow for Multi-Resolution Scientific Data Reduction and Visualization

TL;DR

This paper presents a comprehensive workflow for high-quality multi-resolution data compression and visualization, enabling efficient storage and analysis of large-scale scientific data from both uniform and AMR simulations.

Contribution

It introduces a novel workflow combining ROI extraction, optimized lossy compression, and uncertainty visualization to improve multi-resolution data handling.

Findings

Significant compression quality improvements achieved.

Effective transformation of uniform data into multi-resolution format.

Enhanced understanding of lossy compression impacts through visualization.

Abstract

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.