%0 Conference Paper%T Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs %A Tian, Jiannan; Di, Sheng; Yu, Xiaodong; Rivera, Cody; Zhao, Kai; Jin, Sian; Feng, Yunhe; Liang, Xin; Tao, Dingwen; Cappello, Franck%B 2021 IEEE Cluster (Virtual) (Portland, OR, US, 09/07/2021 - 09/10/2021)%C %D 2021%E %F %G %H %I %K %M %N %P 283 - 293%Q %R 10.1109/Cluster48925.2021.00047%U %V %W %X 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.%Z %1 %8 %9 %+ %^ %> %< %[ %~