Lossy Scientific Data Compression With SPERR - Li, Shaomeng; Lindstrom, Peter; Clyne, John (Proceedings - IEEE International Parallel and Distributed Processing Symposium, 2023)
As the need for data reduction in high-performance computing (HPC) continues to grow, we introduce a new and highly effective tool to help achieve this goal-SPERR. SPERR is a versatile lossy compressor for structured scientific data; it is built on top of an advanced wavelet compression algorithm, SPECK, and provides additional capabilities valued in HPC environments. These capabilities include parallel execution for large volumes and a compression mode that satisfies a maximum point-wise error tolerance. Evaluation shows that in most settings SPERR achieves the best rate-distortion trade-off among current popular lossy scientific data compressors.