Practical Random Linear Network Coding on GPUs

Xiaowen Chu*, Kaiyong Zhao

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

3 Citations (Scopus)


Recently, random linear network coding has been widely applied in peer-to-peer network applications. Instead of sharing the raw data with each other, peers in the network produce and send encoded data to each other. As a result, the communication protocols have been greatly simplified, and the applications experience higher end-to-end throughput and better robustness to network churns. Since it is difficult to verify the integrity of the encoded data, such systems can suffer from the famous pollution attack, in which a malicious node can send bad encoded blocks that consist of bogus data. Consequently, the bogus data will be propagated into the whole network at an exponential rate. Homomorphic hash functions (HHFs) have been designed to defend systems from such pollution attacks, but with a new challenge: HHFs require that network coding must be performed in GF(q), where q is a very large prime number. This greatly increases the computational cost of network coding, in addition to the already computational expensive HHFs. This chapter exploits the potential of the huge computing power of Graphic Processing Units (GPUs) to reduce the computational cost of network coding and homomorphic hashing. With our network coding and HHF implementation on GPU, we observed significant computational speedup in comparison with the best CPU implementation. This implementation can lead to a practical solution for defending the pollution attacks in distributed systems.

Original languageEnglish
Title of host publicationGPU Solutions to Multi-scale Problems in Science and Engineering
EditorsDavid A. Yuen, Long Wang, Xuebin Chi, Lennart Johnsson, Wei Ge, Yaolin Shi
PublisherSpringer Berlin Heidelberg
Number of pages16
ISBN (Electronic)9783642164057
ISBN (Print)9783642164040, 9783662506912
Publication statusPublished - 9 Jan 2013

Publication series

NameLecture Notes in Earth System Sciences
ISSN (Print)2193-8571
ISSN (Electronic)2193-858X

Scopus Subject Areas

  • Computers in Earth Sciences
  • Earth and Planetary Sciences(all)

User-Defined Keywords

  • GPU computing
  • Network coding
  • Pollution attack


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