A framework for compressing unstructured scientific data via serialization

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.

6 pages, 9 figures

References in corpus (7)

• Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization
• TTHRESH: Tensor Compression for Multidimensional Visual Data
• MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
• Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
• ATC: an Advanced Tucker Compression library for multidimensional data
• A General Framework for Error-controlled Unstructured Scientific Data Compression
• Lifting MGARD: construction of (pre)wavelets on the interval using polynomial predictors of arbitrary order