Spatiotemporal Wavelet Compression for Visualization of Scientific Simulation Data

Data reduction through compression is emerging as a promising approach to ease I/O costs for simulation codes on supercomputers. Typically, this compression is achieved by techniques that operate on individual time slices. However, as simulation codes advance in time, outputting multiple time slices as they go, the opportunity for compression incorporating the time dimension has not been extensively explored. Moreover, recent supercomputers are increasingly equipped with deeper memory hierarchies, including solid state drives and burst buffers, which creates the opportunity to temporarily store multiple time slices and then apply compression to them all at once, i.e., spatiotemporal compression. This paper explores the benefits of incorporating the time dimension into existing wavelet compression, including studying its key parameters and demonstrating its benefits in three axes: storage, accuracy, and temporal resolution. Our results demonstrate that temporal compression can improve each of these axes, and that the impact on performance for real systems, including tradeoffs in memory usage and execution time, is acceptable. We also demonstrate the benefits of spatiotemporal wavelet compression with real-world visualization use cases and tailored evaluation metrics.