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

机译:在支持OpenGL的图形卡上实现随机线性网络编码

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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.
机译:本文介绍了在支持OpenGL的图形卡上网络编码的实现。网络编码是一种有趣的方法,可以提高多跳网络的容量和健壮性。当前的问题是在计算能力,能量和存储器受到限制的移动设备上实现随机线性网络编码。一些移动设备配备了3D图形加速器,可用于执行大多数与RLNC相关的计算。这种交叉已经被用于物理或医学等对计算要求很高的研究任务中。作为第一步,本文重点介绍了在台式机和笔记本电脑上使用OpenGL库和NVidia的Cg工具包实现RLNC的方法。几项测量结果表明,图形加速器上的实现性能明显优于CPU。由于GPU的并行特性,在较大的世代大小下,OpenGL实现相对较好。因此,本文为将来在移动设备上执行网络编码提供了一种有吸引力的解决方案。

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