Memory-side acceleration for XML parsing
NPC'11 Proceedings of the 8th IFIP international conference on Network and parallel computing
Benefits of using parallelized non-progressive network coding
Journal of Network and Computer Applications
Exploiting SIMD parallelism on dynamically partitioned parallel network coding for P2P systems
Computers and Electrical Engineering
Design and evaluation of random linear network coding Accelerators on FPGAs
ACM Transactions on Embedded Computing Systems (TECS)
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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 × 128 coefficient matrix and different-sized data blocks.