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Today's scientific high performance computing (HPC) applications or advancedinstruments are producing vast volumes of data across a wide range of domains,which introduces a serious burden on data transfer and storage. Error-boundedlossy compression has been developed and widely used in scientific community,because not only can it significantly reduce the data volumes but it can alsostrictly control the data distortion based on the use-specified error bound.Existing lossy compressors, however, cannot offer ultra-fast compression speed,which is highly demanded by quite a few applications or use-cases (such asin-memory compression and online instrument data compression). In this paper,we propose a novel ultra-fast error-bounded lossy compressor, which can obtainfairly high compression performance on both CPU and GPU, also with reasonablyhigh compression ratios. The key contributions are three-fold: (1) We propose anovel, generic ultra-fast error-bounded lossy compression framework -- calledUFZ, by confining our design to be composed of only super-lightweightoperations such as bitwise and addition/subtraction operation, still keeping acertain high compression ratio. (2) We implement UFZ on both CPU and GPU andoptimize the performance according to their architectures carefully. (3) Weperform a comprehensive evaluation with 6 real-world production-levelscientific datasets on both CPU and GPU. Experiments show that UFZ is 2~16X asfast as the second-fastest existing error-bounded lossy compressor (either SZor ZFP) on CPU and GPU, with respect to both compression and decompression.

SZx: an Ultra-fast Error-bounded Lossy Compressor for Scientific Datasets