  To help understand our universe better, researchers and scientists currently
run extreme-scale cosmology simulations on leadership supercomputers. However,
such simulations can generate large amounts of scientific data, which often
result in expensive costs in data associated with data movement and storage.
Lossy compression techniques have become attractive because they significantly
reduce data size and can maintain high data fidelity for post-analysis. In this
paper, we propose to use GPU-based lossy compression for extreme-scale
cosmological simulations. Our contributions are threefold: (1) we implement
multiple GPU-based lossy compressors to our opensource compression benchmark
and analysis framework named Foresight; (2) we use Foresight to comprehensively
evaluate the practicality of using GPU-based lossy compression on two
real-world extreme-scale cosmology simulations, namely HACC and Nyx, based on a
series of assessment metrics; and (3) we develop a general optimization
guideline on how to determine the best-fit configurations for different lossy
compressors and cosmological simulations. Experiments show that GPU-based lossy
compression can provide necessary accuracy on post-analysis for cosmological
simulations and high compression ratio of 5~15x on the tested datasets, as well
as much higher compression and decompression throughput than CPU-based
compressors.
