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Massive Parallelization for Random Linear Network Coding |
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PP: 571-578 |
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Author(s) |
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Seong-min Choi,
Kyogu Lee,
Joon-Sang Park,
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Abstract |
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| In this paper, we propose a general-purpose graphics processing unit (GPGPU) based parallelization technique for random
linear network coding (RLNC). RLNC is recognized as a useful tool for enhancing performance of networked systems, and several
parallel implementation techniques have been proposed in the literature to overcome its high computation overhead. However, existing
parallel methods cannot take full advantage of GPGPU technology on many occasions. Addressing this problem, we propose a new
RLNC parallelization technique that can exploit GPGPU architectures in full. Our method exhibits as much as a 5x increase in
throughput compared to existing parallel RLNC decoding schemes leveraging GPGPU. |
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