TAC+: Optimizing Error-Bounded Lossy Compression for 3D AMR Simulations

TL;DR

This paper introduces TAC+, a high-dimensional error-bounded lossy compression method for AMR simulation data, significantly improving compression ratios and data fidelity by leveraging multi-level pre-processing and optimization techniques.

Contribution

It proposes a novel high-dimensional SZ-based compression approach for AMR data, with adaptive pre-processing and optimized encoding, outperforming existing methods in compression ratio and data accuracy.

Findings

TAC+ achieves up to 4.9× better compression ratio.

The method reduces data distortion on application-specific metrics.

Experiments on 10 datasets validate its effectiveness.

Abstract

Today's scientific simulations require significant data volume reduction because of the enormous amounts of data produced and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. However, little work has been done to improve error-bounded lossy compression for Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that only leverages 1D compression, in this work, we propose an approach (TAC) to leverage high-dimensional SZ compression for each refinement level of AMR data. To remove the data redundancy across different levels, we propose several pre-process strategies and adaptively use them based on the data features. We further optimize TAC to TAC+ by improving the lossless encoding stage of SZ compression to handle many small AMR data blocks after the pre-processing efficiently. Experiments on 10 AMR datasets from three real-world large-scale AMR simulations demonstrate that TAC+ can improve the compression ratio by up to 4.9$\times$ under the same data distortion, compared to the state-of-the-art method. In addition, we leverage the flexibility of our approach to tune the error bound for each level, which achieves much lower data distortion on two application-specific metrics.