GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders

TL;DR

GraphComp introduces a graph-based error-bounded lossy compression technique for scientific data, leveraging temporal graph autoencoders to effectively capture spatial-temporal correlations and achieve superior compression ratios while maintaining data fidelity.

Contribution

It proposes a novel graph segmentation and neural network approach for scientific data compression that outperforms existing methods in compression ratio.

Findings

Achieves 22-50% higher compression ratios than state-of-the-art methods.

Effectively preserves data within user-defined error bounds.

Utilizes a temporal graph autoencoder to learn compact data representations.

Abstract

The generation of voluminous scientific data poses significant challenges for efficient storage, transfer, and analysis. Recently, error-bounded lossy compression methods emerged due to their ability to achieve high compression ratios while controlling data distortion. However, they often overlook the inherent spatial and temporal correlations within scientific data, thus missing opportunities for higher compression. In this paper we propose GRAPHCOMP, a novel graph-based method for error-bounded lossy compression of scientific data. We perform irregular segmentation of the original grid data and generate a graph representation that preserves the spatial and temporal correlations. Inspired by Graph Neural Networks (GNNs), we then propose a temporal graph autoencoder to learn latent representations that significantly reduce the size of the graph, effectively compressing the original data. Decompression reverses the process and utilizes the learnt graph model together with the latent representation to reconstruct an approximation of the original data. The decompressed data are guaranteed to satisfy a user-defined point-wise error bound. We compare our method against the state-of-the-art error-bounded lossy methods (i.e., HPEZ, SZ3.1, SPERR, and ZFP) on large-scale real and synthetic data. GRAPHCOMP consistently achieves the highest compression ratio across most datasets, outperforming the second-best method by margins ranging from 22% to 50%.