research-article

Massively Parallel Huffman Decoding on GPUs

Authors:

André Weißenberger,

Bertil SchmidtAuthors Info & Claims

ICPP '18: Proceedings of the 47th International Conference on Parallel Processing

Article No.: 27, Pages 1 - 10

https://doi.org/10.1145/3225058.3225076

Published: 13 August 2018 Publication History

Abstract

Data compression is a fundamental building block in a wide range of applications. Besides its intended purpose to save valuable storage on hard disks, compression can be utilized to increase the effective bandwidth to attached storage as realized by state-of-the-art file systems. In the foreseeing future, on-the-fly compression and decompression will gain utmost importance for the processing of data-intensive applications such as streamed Deep Learning tasks or Next Generation Sequencing pipelines, which establishes the need for fast parallel implementations. Huffman coding is an integral part of a number of compression methods. However, efficient parallel implementation of Huffman decompression is difficult due to inherent data dependencies (i.e. the location of a decoded symbol depends on its predecessors). In this paper, we present the first massively parallel decoder implementation that is compatible with Huffman's original method by taking advantage of the self-synchronization property of Huffman codes. Our performance evaluation on three different CUDA-enabled GPUs (TITAN V, TITAN XP, GTX 1080) demonstrates speedups of over one order-of-magnitude compared to the state-of-art CPU-based Zstandard Huffman decoder. Our implementation is available at https://github.com/weissenberger/gpuhd.

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Cited By

Goel RSchütz MNarayanan PKerbl B(2024)Real-Time Decompression and Rasterization of Massive Point CloudsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36753737:3(1-15)Online publication date: 9-Aug-2024
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Weißenberger ASchmidt B(2024)Massively Parallel Inverse Block-sorting Transforms for bzip2 Decompression on GPUsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673067(856-865)Online publication date: 12-Aug-2024
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Massively Parallel Huffman Decoding on GPUs

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cover image ACM Other conferences

ICPP '18: Proceedings of the 47th International Conference on Parallel Processing

August 2018

945 pages

ISBN:9781450365109

DOI:10.1145/3225058

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Oregon: University of Oregon

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Publication History

Published: 13 August 2018

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ICPP 2018

ICPP 2018: 47th International Conference on Parallel Processing

August 13 - 16, 2018

OR, Eugene, USA

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ICPP '18 Paper Acceptance Rate 91 of 313 submissions, 29%;

Overall Acceptance Rate 91 of 313 submissions, 29%

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https://dl.acm.org/doi/10.1145/3675373
Weißenberger ASchmidt B(2024)Massively Parallel Inverse Block-sorting Transforms for bzip2 Decompression on GPUsProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673067(856-865)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673067
Song SHuang YJiang PYu XZheng WDi SCao QFeng YXie ZCappello FMencagli GDazzi PLowenthal DBadia R(2024)CereSZ: Enabling and Scaling Error-bounded Lossy Compression on Cerebras CS-2Proceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658691(309-321)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3625549.3658691
Piochowiak MDachsbacher C(2024)Fast Compressed Segmentation Volumes for Scientific VisualizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332657330:1(12-22)Online publication date: 1-Jan-2024
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Sarangi SBaas B(2023)Energy-efficient canonical Huffman decoders on many-core processor arrays and FPGAsIntegration, the VLSI Journal10.1016/j.vlsi.2022.09.01588:C(156-165)Online publication date: 1-Jan-2023
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