Enabling Efficient Error-Controlled Lossy Compression for Unstructured Scientific Data

Today's scientific applications are producing vast amounts of data with cutting-edge high-performance computing systems and high-resolution instruments, causing severe problems in data transmission and storage. While error-controlled data compression is regarded as a direct way to solve the problem, most existing compressors are designed for data from structured meshes. In this work, we propose a generic framework to enable efficient error-controlled compression for scientific data from unstructured meshes. The contributions are four-fold: (1) We design a prediction-based framework with additional preprocessing stages to better incorporate mesh information. (2) We propose three families of prediction methods for unstructured meshes and integrate them into the framework, which yields high prediction accuracy and thus significantly improves the compression ratios and quality. (3) We enhance our framework by enabling invalid node processing and feature preservation. (4) We evaluate our approaches using five datasets from real-world applications and compare them with state-of-the-art error-controlled lossy compressors. Experiments demonstrate that the proposed compression methods deliver up to $26.36 \times, 8.24 \times$, and $2.56 \times$ compression ratios over existing compressors under the same error bound, Peak Signal-to-Noise Ratios, and critical point preservation levels, respectively. This leads to $1.63 \times$ performance speedup in the end-to-end data transfer on Globus.