Improve the decoding process of rateless erasure code and network coding with graphics processing unit in IoT

The Internet of Things (IoT) is an emerging network that integrates the physical world and cyberspace through wireless technologies. Due to the ever-growing number of connected devices in IoT, the generated data traffic grows exponentially, so do the bad signal-to-noise-ratio and packets collision. For the past decade, such issue is answered with the deployment of rateless erasure code (REC) and network coding (NC), where promising network throughput is obtained with the trade-off in the computational intensive algorithm, namely Gaussian elimination (GE). Many papers, which have attempted to accelerate the computing speed of GE with graphics processing unit (GPU), have neglected the fact that majority of the network traffic are short messages of tens packets in their studies. This paper has found that GPU can further be utilised to decode many messages in parallel, leading to significant speedup. In this paper, we parallelise the GE in terms of XOR row operations, matrix multiplication and bulk decoding of thousand of messages. Our experimental results demonstrated that a speedup of factor of 6 is achievable with the state-of-the-art GPU as compared to XEON E3 CPU.