cuSZp: An Ultra-Fast GPU Error-Bounded Lossy Compression Framework with Optimized End-to-End Performance

Yafan Huang, Sheng Di, Xiaodong Yu, Guanpeng Li, Franck Cappello

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

6 Scopus citations

Abstract

Modern scientific applications and supercomputing systems are generating large amounts of data in various fields, leading to critical challenges in data storage footprints and communication times. To address this issue, error-bounded GPU lossy compression has been widely adopted, since it can reduce the volume of data within a customized threshold on data distortion. In this work, we propose an ultra-fast error-bounded GPU lossy compressor cuSZP. Specifically, cuSZp computes the linear recurrences with hierarchical parallelism to fuse the massive computation into one kernel, drastically improving the end-to-end throughput. In addition, cuSZp adopts a block-wise design along with a lightweight fixed-length encoding and bit-shuffle inside each block such that it achieves high compression ratios and data quality. Our experiments on NVIDIA A100 GPU with 6 representative scientific datasets demonstrate that cuSZp can achieve an ultra-fast end-to-end throughput (95.53x compared with cuSZ) along with a high compression ratio and high reconstructed data quality.

Original languageEnglish
Title of host publicationSC 2023 - International Conference for High Performance Computing, Networking, Storage and Analysis
ISBN (Electronic)9798400701092
DOIs
StatePublished - 2023
Event2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023 - Denver, United States
Duration: 12 Nov 202317 Nov 2023

Publication series

NameInternational Conference for High Performance Computing, Networking, Storage and Analysis, SC
ISSN (Print)2167-4329
ISSN (Electronic)2167-4337

Conference

Conference2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023
Country/TerritoryUnited States
CityDenver
Period12/11/2317/11/23

Keywords

  • CUDA
  • Error-bounded Lossy Compression
  • GPU
  • High-speed Compressor
  • Parallel Computing
  • Scientific Simulation

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