research-article

ndzip-gpu: efficient lossless compression of scientific floating-point data on GPUs

Authors:

Thomas FahringerAuthors Info & Claims

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Article No.: 93, Pages 1 - 14

https://doi.org/10.1145/3458817.3476224

Published: 13 November 2021 Publication History

Abstract

Lossless data compression is a promising software approach for reducing the bandwidth requirements of scientific applications on accelerator clusters without introducing approximation errors. Suitable compressors must be able to effectively compact floating-point data while saturating the system interconnect to avoid introducing unnecessary latencies.

We present ndzip-gpu, a novel, highly-efficient GPU parallelization scheme for the block compressor ndzip, which has recently set a new milestone in CPU floating-point compression speeds.

Through the combination of intra-block parallelism and efficient memory access patterns, ndzip-gpu achieves high resource utilization in decorrelating multi-dimensional data via the Integer Lorenzo Transform. We further introduce a novel, efficient warp-cooperative primitive for vertical bit packing, providing a high-throughput data reduction and expansion step.

Using a representative set of scientific data, we compare the performance of ndzip-gpu against five other, existing GPU compressors. While observing that effectiveness of any compressor strongly depends on characteristics of the dataset, we demonstrate that ndzip-gpu offers the best average compression ratio for the examined data. On Nvidia Turing, Volta and Ampere hardware, it achieves the highest single-precision throughput by a significant margin while maintaining a favorable trade-off between data reduction and throughput in the double-precision case.

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Cited By

Chen XTian JBeaver IFreeman CYan YWang JTao D(2024)FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point DataProceedings of the VLDB Endowment10.14778/3648160.364818017:6(1418-1431)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648180
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
Li FLuo HWang JLi YTang ZLi K(2024)AMP: Total Variation Reduction for Lossless Compression via Approximate Median-based PreconditioningACM Transactions on Embedded Computing Systems10.1145/360535923:6(1-22)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3605359
Show More Cited By

Index Terms

ndzip-gpu: efficient lossless compression of scientific floating-point data on GPUs
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
2. Theory of computation
  1. Design and analysis of algorithms
    1. Data structures design and analysis
      1. Data compression

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cover image ACM Conferences

SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

November 2021

1493 pages

ISBN:9781450384421

DOI:10.1145/3458817

General Chair:
Bronis R. de Supinski,
Program Chairs:
Mary Hall,
Todd Gamblin

Copyright © 2021 ACM.

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Published: 13 November 2021

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SC '21: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 14 - 19, 2021

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Cited By

Chen XTian JBeaver IFreeman CYan YWang JTao D(2024)FCBench: Cross-Domain Benchmarking of Lossless Compression for Floating-Point DataProceedings of the VLDB Endowment10.14778/3648160.364818017:6(1418-1431)Online publication date: 3-May-2024
https://dl.acm.org/doi/10.14778/3648160.3648180
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
Li FLuo HWang JLi YTang ZLi K(2024)AMP: Total Variation Reduction for Lossless Compression via Approximate Median-based PreconditioningACM Transactions on Embedded Computing Systems10.1145/360535923:6(1-22)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3605359
Lin FArunruangsirilert KSun HKatto J(2023)Recoil: Parallel rANS Decoding with Decoder-Adaptive ScalabilityProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605588(31-40)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605588
Hu YZhang FXia YYao ZZeng LDing HWei ZZhang XZhai JDu XMa S(2023)Enabling Efficient Random Access to Hierarchically Compressed Text Data on Diverse GPU PlatformsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.329434134:10(2699-2717)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TPDS.2023.3294341
Ratsaby JTimashkov A(2023)Accelerating the LZ-complexity algorithm2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)10.1109/ICPADS60453.2023.00038(200-207)Online publication date: 17-Dec-2023
https://doi.org/10.1109/ICPADS60453.2023.00038
Zhang FHu YDing HYao ZWei ZZhang XDu X(2022)Optimizing Random Access to Hierarchically-Compressed Data on GPUSC22: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41404.2022.00023(1-15)Online publication date: Nov-2022
https://doi.org/10.1109/SC41404.2022.00023

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