A framework for compressing unstructured scientific data via serialization

TL;DR

This paper introduces a flexible framework for compressing unstructured scientific data by reordering data points based on mesh connectivity, enhancing compatibility with existing compression algorithms and improving efficiency.

Contribution

The paper proposes a topology-preserving reordering method that can be integrated with various compression algorithms for unstructured data, supporting both offline and online processing.

Findings

Effective reordering improves compression ratios.

Compatible with multiple compression algorithms like MGARD, SZ, ZFP.

Demonstrated on large-scale real datasets.

Abstract

We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm's greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP.