Data Compression for the Exascale Computing Era - Survey

While periodic checkpointing has been an important mechanism for tolerating faults in high-performance computing (HPC) systems, it is cost-prohibitive as the HPC system approaches exascale. Applying compression techniques is one common way to mitigate such burdens by reducing the data size, but they are often found to be less effective for scientific datasets. Traditional lossless compression techniques that look for repeated patterns are ineffective for scientific data in which high-precision data is used and hence common patterns are rare to find. In this paper, we present a comparison of several lossless and lossy data compression algorithms and discuss their methodology under the exascale environment. As data volume increases, we discover an increasing trend of new domain-driven algorithms that exploit the inherent characteristics exhibited in many scientific dataset, such as relatively small changes in data values from one simulation iteration to the next or among neighboring data. In particular, significant data reduction has been observed in lossy compression. This paper also discusses how the errors introduced by lossy compressions are controlled and the tradeoffs with the compression ratio.