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We explore an error-bounded lossy compression approach for reducingscientific data associated with 2D/3D unstructured meshes. While existing lossycompressors offer a high compression ratio with bounded error for regular griddata, methodologies tailored for unstructured mesh data are lacking; forexample, one can compress nodal data as 1D arrays, neglecting the spatialcoherency of the mesh nodes. Inspired by the SZ compressor, which predicts andquantizes values in a multidimensional array, we dynamically reorganize nodaldata into sequences. Each sequence starts with a seed cell; based on apredefined traversal order, the next cell is added to the sequence if thecurrent cell can predict and quantize the nodal data in the next cell with thegiven error bound. As a result, one can efficiently compress the quantizednodal data in each sequence until all mesh nodes are traversed. This paper alsointroduces a suite of novel error metrics, namely continuous mean squared error(CMSE) and continuous peak signal-to-noise ratio (CPSNR), to assess compressionresults for unstructured mesh data. The continuous error metrics are defined byintegrating the error function on all cells, providing objective statisticsacross nonuniformly distributed nodes/cells in the mesh. We evaluate ourmethods with several scientific simulations ranging from ocean-climate modelsand computational fluid dynamics simulations with both traditional andcontinuous error metrics. We demonstrated superior compression ratios andquality than existing lossy compressors.

A Prediction-Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error