Advancing Scientific Data Compression via Cross-Field Prediction

Scientific applications generate massive amounts of data, leading to significant storage and I/O bottlenecks. Lossy compression is a crucial technique for reducing data volume in scientific computing, balancing storage efficiency and data fidelity. Such scientific data often consists of multiple fields representing various physical metrics, which exhibit inherent correlations. However, existing compression solutions predominantly rely on local information within a single field, overlooking cross-field correlations that could enhance compression efficiency. In this paper, we introduce a novel compression framework that integrates both local and cross-field information to enhance predictive accuracy. With the help of a carefully designed neural network, our approach effectively captures complex correlations across fields, significantly improving compression ratios. Additionally, our method can rely solely on cross-field prediction to reconstruct fields, achieving extremely high compression ratios under relaxed accuracy constraints. We evaluate our approach on two real-world scientific applications across 85 data fields, demonstrating compression ratio improvements of up to 103.4% for a single field and up to 19.3% overall.