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Massive parallelization technique for random linear network coding

Authors: Seong-Min Choi; Joon-Sang Park;

Massive parallelization technique for random linear network coding

Abstract

Random linear network coding (RLNC) has gain popularity as a useful performance-enhancing tool for communications networks. In this paper, we propose a RLNC parallel implementation technique for General Purpose Graphical Processing Units (GPGPUs.) Recently, GPGPU technology has paved the way for parallelizing RLNC; however, current state-of-the-art parallelization techniques for RLNC are unable to fully utilize GPGPU technology in many occasions. Addressing this problem, we propose a new RLNC parallelization technique that can fully exploit GPGPU architectures. Our parallel method shows over 4 times higher throughput compared to existing state-of-the-art parallel RLNC decoding schemes for GPGPU and 20 times higher throughput over the state-of-the-art serial RLNC decoders.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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