题目/Title：Scalable Packet Classification via GPU Metaprogramming
作者/Author：亢康,邓仰东
Kang Kang,Yangdong Deng
会议/Conference：DATE 2011
地点/Location：Grenoble, France
年份/Issue Date：2011.14-18 March
页码/pages：pp. 1 - 4
摘要/Abstract：
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.