A framework for compressing unstructured scientific data via serialization

• URL: http://arxiv.org/abs/2410.08059v1
• Date: Thu, 10 Oct 2024 15:53:35 GMT
• Title: A framework for compressing unstructured scientific data via serialization
• Authors: Viktor Reshniak, Qian Gong, Rick Archibald, Scott Klasky, Norbert Podhorszki,
• Abstract summary: 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.
• Score: 2.5768995309704104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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