  Error-bounded lossy compression is a critical technique for significantly
reducing scientific data volumes. With ever-emerging heterogeneous
high-performance computing (HPC) architecture, GPU-accelerated error-bounded
compressors (such as cuSZ+ and cuZFP) have been developed. However, they suffer
from either low performance or low compression ratios. To this end, we propose
cuSZ+ to target both high compression ratios and throughputs. We identify that
data sparsity and data smoothness are key factors for high compression
throughputs. Our key contributions in this work are fourfold: (1) We propose an
efficient compression workflow to adaptively perform run-length encoding and/or
variable-length encoding. (2) We derive Lorenzo reconstruction in decompression
as multidimensional partial-sum computation and propose a fine-grained Lorenzo
reconstruction algorithm for GPU architectures. (3) We carefully optimize each
of cuSZ+ kernels by leveraging state-of-the-art CUDA parallel primitives. (4)
We evaluate cuSZ+ using seven real-world HPC application datasets on V100 and
A100 GPUs. Experiments show cuSZ+ improves the compression throughputs and
ratios by up to 18.4X and 5.3X, respectively, over cuSZ on the tested datasets.
