Optimizing Error-Bounded Lossy Compression for Scientific Data on GPUs

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

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 languageEnglish
Title of host publicationProceedings - 2021 IEEE International Conference on Cluster Computing, Cluster 2021
Pages283-293
Number of pages11
ISBN (Electronic)9781728196664
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Cluster Computing, Cluster 2021 - Virtual, Portland, United States
Duration: Sep 7 2021Sep 10 2021

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2021-September
ISSN (Print)1552-5244

Conference

Conference2021 IEEE International Conference on Cluster Computing, Cluster 2021
Country/TerritoryUnited States
CityVirtual, Portland
Period9/7/219/10/21

Bibliographical note

Publisher Copyright:
©2021 IEEE.

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Signal Processing

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