Pipelined parallel LZSS for streaming data compression on GPGPUs

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

27 Citations (Scopus)

Abstract

In this paper, we present an algorithm and provide design improvements needed to port the serial Lempel-Ziv-Storer-Szymanski (LZSS), lossless data compression algorithm, to a parallelized version suitable for general purpose graphic processor units (GPGPU), specifically for NVIDIA's CUDA Framework. The two main stages of the algorithm, substring matching and encoding, are studied in detail to fit into the GPU architecture. We conducted detailed analysis of our performance results and compared them to serial and parallel CPU implementations of LZSS algorithm. We also benchmarked our algorithm in comparison with well known, widely used programs; GZIP and ZLIB. We achieved up to 34x better throughput than the serial CPU implementation of LZSS algorithm and up to 2.21x better than the parallelized version.

Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE 18th International Conference on Parallel and Distributed Systems, ICPADS 2012
Pages37-44
Number of pages8
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event18th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2012 - Singapore, Singapore
Duration: 17 Dec 201219 Dec 2012

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
ISSN (Print)1521-9097

Conference

Conference18th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2012
Country/TerritorySingapore
CitySingapore
Period17/12/1219/12/12

Keywords

  • CUDA
  • GPU
  • LZSS
  • Lossless data compression

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