Error Analysis of ZFP Compression for Floating-Point Data
For HPC practitioners needing guaranteed error bounds when using lossy compression for floating-point data, this work provides rigorous theoretical guarantees that were previously lacking.
This paper provides a theoretical error analysis for ZFP, a state-of-the-art lossy compression algorithm for floating-point data, establishing bounds on round-off error for all three compression modes (fixed rate, fixed accuracy, fixed precision). Numerical tests confirm the accuracy of the derived bounds.
Compression of floating-point data will play an important role in high-performance computing as data bandwidth and storage become dominant costs. Lossy compression of floating-point data is powerful, but theoretical results are needed to bound its errors when used to store look-up tables, simulation results, or even the solution state during the computation. \black{In this paper, we analyze the round-off error introduced by ZFP, a %state-of-the-art lossy compression algorithm.} The stopping criteria for ZFP depends on the compression mode specified by the user; either fixed rate, fixed accuracy, or fixed precision [P. Lindstrom, Fixed-rate compressed floating-point arrays, IEEE Transactions on Visualization and Computer Graphics, 2014]. While most of our discussion is focused on the fixed precision mode of ZFP, we establish a bound on the error introduced by all three compression modes. In order to tightly capture the error, we first introduce a vector space that allows us to work with binary representations of components. Under this vector space, we define operators that implement each step of the ZFP compression and decompression to establish a bound on the error caused by ZFP. To conclude, numerical tests are provided to demonstrate the accuracy of the established bounds.