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Scalable packet classification via GPU metaprogramming

Kang Kang, Yangdong Steve Deng
Institute of Microelectronics, Tsinghua University
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2011

@inproceedings{kang2011scalable,

   title={Scalable packet classification via GPU metaprogramming},

   author={Kang, K. and Deng, Y.S.},

   booktitle={Design, Automation & Test in Europe Conference & Exhibition (DATE), 2011},

   pages={1–4},

   organization={IEEE},

   year={2011}

}

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Packet classification has been a fundamental processing pattern of modern networking devices. Today’s high-performance routers use specialized hardware for packet classification, but such solutions suffer from prohibitive cost, high power consumption, and poor extensibility. On the other hand, software-based routers offer the best flexibility, but could only deliver limited performance (<10Gbps). Recently, graphics processing units (GPUs) have been proved to be an efficient accelerator for software routers. In this work, we propose a GPU-based linear search framework for packet classification. The core of our framework is a metaprogramming technique that dramatically enhances the execution efficiency. Experimental results prove that our solution could outperform a CPU-based solution by a factor of 17, in terms of classification throughput. Our technique is scalable to large rule sets consisting of over 50K rules and thus provides a solid foundation for future applications of packet context inspection.
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