  Today's large-scale scientific applications running on high-performance
computing (HPC) systems generate vast data volumes. Thus, data compression is
becoming a critical technique to mitigate the storage burden and data-movement
cost. However, existing lossy compressors for scientific data cannot achieve a
high compression ratio and throughput simultaneously, hindering their adoption
in many applications requiring fast compression, such as in-memory compression.
To this end, in this work, we develop a fast and high-ratio error-bounded lossy
compressor on GPUs for scientific data (called FZ-GPU). Specifically, we first
design a new compression pipeline that consists of fully parallelized
quantization, bitshuffle, and our newly designed fast encoding. Then, we
propose a series of deep architectural optimizations for each kernel in the
pipeline to take full advantage of CUDA architectures. We propose a warp-level
optimization to avoid data conflicts for bit-wise operations in bitshuffle,
maximize shared memory utilization, and eliminate unnecessary data movements by
fusing different compression kernels. Finally, we evaluate FZ-GPU on two NVIDIA
GPUs (i.e., A100 and RTX A4000) using six representative scientific datasets
from SDRBench. Results on the A100 GPU show that FZ-GPU achieves an average
speedup of 4.2X over cuSZ and an average speedup of 37.0X over a multi-threaded
CPU implementation of our algorithm under the same error bound. FZ-GPU also
achieves an average speedup of 2.3X and an average compression ratio
improvement of 2.0X over cuZFP under the same data distortion.
