GPULZ: Optimizing LZSS Lossless Compression for Multi-byte Data on Modern GPUs

Boyuan Zhang, Jiannan Tian, Sheng Di, Xiaodong Yu, Martin Swany, Dingwen Tao, Franck Cappello

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

7 Scopus citations

Abstract

Today's graphics processing unit (GPU) applications produce vast volumes of data, which are challenging to store and transfer efficiently. Thus, data compression is becoming a critical technique to mitigate the storage burden and communication cost. LZSS is the core algorithm in many widely used compressors, such as Deflate. However, existing GPU-based LZSS compressors suffer from low throughput due to the sequential nature of the LZSS algorithm. Moreover, many GPU applications produce multi-byte data (e.g., int16/int32 index, floating-point numbers), while the current LZSS compression only takes single-byte data as input. To this end, in this work, we propose gpuLZ, a highly efficient LZSS compression on modern GPUs for multi-byte data. The contribution of our work is fourfold: First, we perform an in-depth analysis of existing LZ compressors for GPUs and investigate their main issues. Then, we propose two main algorithm-level optimizations. Specifically, we (1) change prefix sum from one pass to two passes and fuse multiple kernels to reduce data movement between shared memory and global memory, and (2) optimize existing pattern-matching approach for multi-byte symbols to reduce computation complexity and explore longer repeated patterns. Third, we perform architectural performance optimizations, such as maximizing shared memory utilization by adapting data partitions to different GPU architectures. Finally, we evaluate gpuLZ on six datasets of various types with NVIDIA A100 and A4000 GPUs. Results show that gpuLZ achieves up to 272.1× speedup on A4000 and up to 1.4× higher compression ratio compared to state-of-the-art solutions.

Original languageEnglish
Title of host publicationACM ICS 2023 - Proceedings of the International Conference on Supercomputing
Pages348-359
Number of pages12
ISBN (Electronic)9798400700569
DOIs
StatePublished - 21 Jun 2023
Event37th ACM International Conference on Supercomputing, ICS 2023 - Orlando, United States
Duration: 21 Jun 202323 Jun 2023

Publication series

NameProceedings of the International Conference on Supercomputing

Conference

Conference37th ACM International Conference on Supercomputing, ICS 2023
Country/TerritoryUnited States
CityOrlando
Period21/06/2323/06/23

Keywords

  • GPU
  • LZSS
  • lossless compression
  • performance

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