  With ever-increasing volumes of scientific floating-point data being produced
by high-performance computing applications, significantly reducing scientific
floating-point data size is critical, and error-controlled lossy compressors
have been developed for years. None of the existing scientific floating-point
lossy data compressors, however, support effective fixed-ratio lossy
compression. Yet fixed-ratio lossy compression for scientific floating-point
data not only compresses to the requested ratio but also respects a
user-specified error bound with higher fidelity. In this paper, we present
FRaZ: a generic fixed-ratio lossy compression framework respecting
user-specified error constraints. The contribution is twofold. (1) We develop
an efficient iterative approach to accurately determine the appropriate error
settings for different lossy compressors based on target compression ratios.
(2) We perform a thorough performance and accuracy evaluation for our proposed
fixed-ratio compression framework with multiple state-of-the-art
error-controlled lossy compressors, using several real-world scientific
floating-point datasets from different domains. Experiments show that FRaZ
effectively identifies the optimum error setting in the entire error setting
space of any given lossy compressor. While fixed-ratio lossy compression is
slower than fixed-error compression, it provides an important new lossy
compression technique for users of very large scientific floating-point
datasets.
