Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

Conference · Thu Dec 31 23:00:00 EST 2020

DOI:https://doi.org/10.1109/Cluster48925.2021.00047· OSTI ID:1864152

Tian, Jiannan; Di, Sheng; Yu, Xiaodong; Rivera, Cody; Zhao, Kai; Jin, Sian; Feng, Yunhe; Liang, Xin; Tao, Dingwen; Cappello, Franck

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.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: USDOE Exascale Computing Project (ECP); USDOE Office of Science; National Science Foundation (NSF)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 1864152
• Country of Publication: United States
• Language: English