Implementation of random linear network coding on OpenGL-enabled graphics cards

Abstract

This paper describes the implementation of network coding on OpenGL-enabled graphics cards. Network coding is an interesting approach to increase the capacity and robustness in multi-hop networks. The current problem is to implement random linear network coding on mobile devices which are limited in computational power, energy, and memory. Some mobile devices are equipped with a 3D graphics accelerator, which could be used to do most of the RLNC related calculations. Such a cross-over have already been used in computationally demanding research tasks as in physics or medicine. As a first step the paper focuses on the implementation of RLNC using the OpenGL library and NVidia's Cg toolkit on desktop PCs and laptops. Several measurement results show that the implementation on the graphics accelerator is outperforming the CPU by a significant margin. The OpenGL implementation performs relatively better with larger generation sizes due to the parallel nature of GPUs. Therefore the paper shows an appealing solution for the future to perform network coding on mobile devices.