  Today's scientific high performance computing (HPC) applications or advanced
instruments are producing vast volumes of data across a wide range of domains,
which introduces a serious burden on data transfer and storage. Error-bounded
lossy compression has been developed and widely used in scientific community,
because not only can it significantly reduce the data volumes but it can also
strictly 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 as
in-memory compression and online instrument data compression). In this paper,
we propose a novel ultra-fast error-bounded lossy compressor, which can obtain
fairly high compression performance on both CPU and GPU, also with reasonably
high compression ratios. The key contributions are three-fold: (1) We propose a
novel, generic ultra-fast error-bounded lossy compression framework -- called
UFZ, by confining our design to be composed of only super-lightweight
operations such as bitwise and addition/subtraction operation, still keeping a
certain high compression ratio. (2) We implement UFZ on both CPU and GPU and
optimize the performance according to their architectures carefully. (3) We
perform a comprehensive evaluation with 6 real-world production-level
scientific datasets on both CPU and GPU. Experiments show that UFZ is 2~16X as
fast as the second-fastest existing error-bounded lossy compressor (either SZ
or ZFP) on CPU and GPU, with respect to both compression and decompression.
