  Because of the vast volume of data being produced by today's scientific
simulations, lossy compression allowing user-controlled information loss can
significantly reduce the data size and the I/O burden. However, for large-scale
cosmology simulation, such as the Hardware/Hybrid Accelerated Cosmology Code
(HACC), where memory overhead constraints restrict compression to only one
snapshot at a time, the lossy compression ratio is extremely limited because of
the fairly low spatial coherence and high irregularity of the data. In this
work, we propose a pattern-matching (similarity searching) technique to
optimize the prediction accuracy and compression ratio of SZ lossy compressor
on the HACC data sets. We evaluate our proposed method with different
configurations and compare it with state-of-the-art lossy compressors.
Experiments show that our proposed optimization approach can improve the
prediction accuracy and reduce the compressed size of quantization codes
compared with SZ. We present several lessons useful for future research
involving pattern-matching techniques for lossy compression.
