Abstract
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 18.4× and 5.3×, respectively, over CUSZ on the tested datasets.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021 |
Pages | 283-293 |
Number of pages | 11 |
ISBN (Electronic) | 9781728196664 |
DOIs | |
State | Published - 2021 |
Event | 2021 IEEE International Conference on Cluster Computing, Cluster 2021 - Virtual, Portland, United States Duration: Sep 7 2021 → Sep 10 2021 |
Publication series
Name | Proceedings - IEEE International Conference on Cluster Computing, ICCC |
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Volume | 2021-September |
ISSN (Print) | 1552-5244 |
Conference
Conference | 2021 IEEE International Conference on Cluster Computing, Cluster 2021 |
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Country/Territory | United States |
City | Virtual, Portland |
Period | 9/7/21 → 9/10/21 |
Bibliographical note
Publisher Copyright:©2021 IEEE.
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Signal Processing