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Error Analysis of ZFP Compression for Floating-Point Data

Journal Article · · SIAM Journal on Scientific Computing
DOI:https://doi.org/10.1137/18M1168832· OSTI ID:1572236
 [1];  [2];  [2];  [2];  [2]
  1. The Univ. of Florida, Gainesville, FL (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
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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. In this paper, we analyze the round-off error introduced by ZFP, a lossy compression algorithm. Here, the stopping criteria for ZFP depends on the compression mode specified by the user: fixed rate, fixed accuracy, or fixed precision [P. Lindstrom, ZFP 0.5.3 Documentation, 2018]. 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, first we 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.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1572236
Report Number(s):
LLNL-JRNL--744818; 900098
Journal Information:
SIAM Journal on Scientific Computing, Journal Name: SIAM Journal on Scientific Computing Journal Issue: 3 Vol. 41; ISSN 1064-8275
Publisher:
SIAMCopyright Statement
Country of Publication:
United States
Language:
English

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Evaluating lossy data compression on climate simulation data within a large ensemble journal January 2016

Cited By (1)

Compression Challenges in Large Scale Partial Differential Equation Solvers journal September 2019

Figures / Tables (17)

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Related Subjects

97 MATHEMATICS AND COMPUTING
error bounds
floating-point representation
lossy compression