Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs

Cody Rivera, Sheng Di, Jiannan Tian, Xiaodong Yu, Dingwen Tao, Franck Cappello

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU's power. At present, cuSZ's compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated loss-less compression step-a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64× over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43× on average.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages717-727
Number of pages11
ISBN (Electronic)9781665481069
DOIs
StatePublished - 2022
Event36th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022 - Virtual, Online, France
Duration: May 30 2022Jun 3 2022

Publication series

NameProceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022

Conference

Conference36th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2022
Country/TerritoryFrance
CityVirtual, Online
Period5/30/226/3/22

Keywords

  • Compression
  • CUDA
  • GPU
  • Huffman Coding
  • Performance
  • Scientific Data Reduction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Computer Science Applications

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