Today’s HPC applications are producing extremely large amounts of data, thus it is necessary to use an efficient compression before storing them to parallel file systems.
We developed the error-bounded HPC data compressor, by proposing a novel HPC data compression method that works very effectively on compressing large-scale HPC data sets.
The compression method starts by linearizing multi-dimensional snapshot data. The key idea is to fit/predict the successive data points with the bestfit selection of curve fitting models. The data that can be predicted precisely will be replaced by the code of the corresponding curve-fitting model. As for the unpredictable data that cannot be approximated by curve-fitting models, we perform an optimized lossy compression via a binary representation analysis.
The key features of SZ are listed below.
1. Input: a data set (or a floating-point array with any dimensions) ; Output: the compressed byte stream
2. SZ supports C, Fortran, and Java.
3. SZ supports two types of error bounds. The users can set either absolute error bound or relative error bound, or a combination of the two bounds （with operator AND or OR).