计算机工程与应用Issue(21)：79-84,6.DOI:10.3778/j.issn.1002-8331.1403-0071
面向众核GPU加速系统的网络编码并行化及优化
Parallelizing network coding on manycore GPU-accelerated system with optimization
摘要
Abstract
It is well known that network coding has emerged as a promising technique to improve network throughput, balance network loads as well as better utilization of the available bandwidth of networks, in which intermediate nodes are allowed to perform processing operations on the incoming packets other than forwarding packets. But, its potential for practical use has remained to be a challenge, due to its high computational complexity which also severely damages its performance. However, system accelerated by many-core GPU can advance network coding with powerful computing capacity and optimized memory hierarchy from GPU. A fragment-based parallel coding and texture-based parallel decoding are proposed on CUDA-enable GPU. Moreover, random linear coding is parallelizing using CUDA with optimization based on proposed techniques. Experimental results demonstrate a remarkable performance improvement, and prove that it is extraordinarily effective to parallelize network coding on many-core GPU-accelerated system.关键词
网络编码/图形处理器(GPU)/并行/计算统一设备架构(CUDA)/优化Key words
network coding/Graphic Processing Unit(GPU)/parallelizing/Compute Unified Device Architecture(CUDA)/optimization分类
信息技术与安全科学引用本文复制引用
唐绍华..面向众核GPU加速系统的网络编码并行化及优化[J].计算机工程与应用,2014,(21):79-84,6.基金项目
国家高技术研究发展计划（863）（No.2012AA010905）；国家自然科学基金（No.60803041，No.61070037）；湖南省教育厅2012年度科技项目（No.12C1024）。 （）
