CuSZ: An efficient GPU-based error-bounded lossy compression framework for scientific data

Jiannan Tian, Sheng Di, Kai Zhao, Cody Rivera, Megan Hickman Fulp, Robert Underwood, Sian Jin, Xin Liang, Jon Calhoun, Dingwen Tao, Franck Cappello

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

18 Scopus citations

Abstract

Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelityfor postanalysis. Because supercomputers and HPC applicationsare becoming heterogeneous using accelerator-based architectures,in particular GPUs, several development teams have recently released GPU versions of their lossy compressors. However, existingstate-of-the-art GPU-based lossy compressors suffer from eitherlow compression and decompression throughput or low compression quality. In this paper, we present an optimized GPU version,cuSZ, for one of the best error-bounded lossy compressors-SZ.To the best of our knowledge, cuSZ is the first error-boundedlossy compressor on GPUs for scientific data. Our contributions arefourfold. (1) We propose a dual-qantization scheme to entirelyremove the data dependency in the prediction step of SZ such thatthis step can be performed very efficiently on GPUs. (2) We developan efficient customized Huffman coding for the SZ compressor onGPUs. (3) We implement cuSZ using CUDA and optimize its performance by improving the utilization of GPU memory bandwidth. (4)We evaluate our cuSZ on five real-world HPC application datasetsfrom the Scientific Data Reduction Benchmarks and compare it withother state-of-the-art methods on both CPUs and GPUs. Experiments show that our cuSZ improves SZ's compression throughputby up to 370.1× and 13.1×, respectively, over the production version running on single and multiple CPU cores, respectively, whilegetting the same quality of reconstructed data. It also improves thecompression ratio by up to 3.48× on the tested data compared withanother state-of-the-art GPU supported lossy compressor.

Original languageEnglish
Title of host publicationPACT 2020 - Proceedings of the ACM International Conference on Parallel Architectures and Compilation Techniques
Pages3-15
Number of pages13
ISBN (Electronic)9781450380751
DOIs
StatePublished - Sep 30 2020
Event2020 ACM International Conference on Parallel Architectures and Compilation Techniques, PACT 2020 - Virtual, Online, United States
Duration: Oct 3 2020Oct 7 2020

Publication series

NameParallel Architectures and Compilation Techniques - Conference Proceedings, PACT
ISSN (Print)1089-795X

Conference

Conference2020 ACM International Conference on Parallel Architectures and Compilation Techniques, PACT 2020
Country/TerritoryUnited States
CityVirtual, Online
Period10/3/2010/7/20

Bibliographical note

Funding Information:
This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations - the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation’s exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357. This work was also supported by the National Science Foundation under Grants CCF-1619253, OAC-2003709, OAC-1948447/2034169, and OAC-2003624/2042084. We would like to thank The University of Alabama for providing the startup funding for this work.

Funding Information:
This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations - the Office of Science and the National Nuclear Security Administration, responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering and early testbed platforms, to support the nation's exascale computing imperative. The material was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357. This work was also supported by the National Science Foundation under Grants CCF-1619253, OAC- 2003709, OAC-1948447/2034169, and OAC-2003624/2042084. We would like to thank The University of Alabama for providing the startup funding for this work.

Publisher Copyright:
© 2020 Association for Computing Machinery.

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture

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