Evaluating the Efficacy of Wavelet Compression for Turbulent-Flow Data Visualization

We explore the ramifications of using wavelet compression on turbulent-flow data from scientific simulations. As upcoming I/O constraints may significantly hamper the ability of scientific simulations to write full-resolution data to disk, we feel this study enhances the understanding of exascale science with respect to potentially applying wavelets in situ. Our approach repeats existing analyses with wavelet-compressed data, using evaluations that are quantitatively based. The data sets we select are large, including one with a 4,096 grid. Our findings show that the efficacy of wavelets vary across the analyses, and that prioritized coefficient compression is consistently superior to a multi-resolution approach, and that the biorthogonal kernels CDF 9/7 and CDF 8/4 perform better than the Haar kernel.