报文分类是网络设备的基本处理模式,通常采用报文过滤系统对每个报文进行分类。传统报文分类难以适应当今越来越高的网络流量,分类处理速度低于报文到达网络接口的速度,无法实现实时分析。因此,本文提出使用GPU对大规模报文集进行并行分类的方法,利用GPU的线程级并行处理能力加速报文分类吞吐率,并对其性能及优化方法进行详细分析。实验结果表明,GPU加速的Linear Search和RFC报文分类算法与纯CPU系统执行相比可达到4.4~132.5倍的加速比。%Packet classification, which is the basic processing model of network devices, commonly uses packet filtering system to classify each message. Traditional packet classification is difficult to adapt to today’s increasingly high network traffic. Its classifi-cation processing speed is lower than the speed of packet to reach the network interface, so that it cannot achieve real-time analy-sis. Therefore, we proposes a method that uses GPU to classify the large scale packet set parallelly. The thread-level parallel pro-cessing capability of GPU is made use to accelerate packet classification throughput. Its performance and optimization methods are analyzed in detail. The experimental results show that compared with processing by pure CPU system, GPU-accelerated Linear Search and RFC algorithm achieve a 4. 4x~132. 5x speedup.
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