Accelerated Network Coding with Dynamic Stream Decomposition on Graphics Processing Unit
Network coding, a well-known technique for optimizing data-flow in wired and wireless network systems, has attracted considerable attention in various fields. However, the decoding complexity in network coding becomes a major performance bottleneck in the practical network systems; thus, several researches have been conducted for improving the decoding performance in network coding. Nevertheless, previously proposed parallel network coding algorithms have shown limited scalability and performance imbalance for different-sized transfer units and multiple streams. In this paper, we propose a new parallel decoding algorithm for network coding using a graphics processing unit (GPU). This algorithm can simultaneously process multiple incoming streams and can maintain its maximum decoding performance irrespective of the size and number of transfer units. Our experimental results show that the proposed algorithm exhibits a 682.2 Mbps decoding bandwidth on a system with GeForce GTX 285 GPU and speed-ups of up to 26 as compared to the existing single stream decoding procedure with a 128 x 128 coefficient matrix and different-sized data blocks.
network coding progressive decoding general-purpose computation on gpu parallel algorithm