A highly parallelized decoder for random network coding leveraging GPGPU

Joon Sang Park, Seung Jun Baek, Kyogu Lee

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Network coding has been shown to improve various performance metrics in computer networks. However, the use of network coding, especially random linear network coding, incurs serious time delay in the decoding process and thus it is imperative to use a network coding implementation that has low decoding latency characteristics, e.g. a parallelized implementation. In this paper, we investigate the problem of parallelizing Pipeline network coding, a variant of random linear coding recently developed in order to alleviate the problems of random linear coding. We propose a novel massively parallelized decoding algorithm leveraging General Purpose Graphics Processing Unit (GPGPU) and show its performance enhancement by up to 100% compared with previous GPGPU-based parallel algorithms via experiments on real systems.

Original languageEnglish
Pages (from-to)233-240
Number of pages8
JournalComputer Journal
Volume57
Issue number2
DOIs
Publication statusPublished - 2014 Feb 1

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Keywords

  • GPGPU
  • Network coding
  • Parallelization

ASJC Scopus subject areas

  • Computer Science(all)

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