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Evaluating GPUs for Network Packet Signature Matching

机译:评估网络数据包签名匹配的GPU

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Modern network devices employ deep packet inspection to enable sophisticated services such as intrusion detection, traffic shaping, and load balancing. At the heart of such services is a signature matching engine that must match packet payloads to multiple signatures at line rates. However, the recent transition to complex regular-expression based signatures coupled with ever-increasing network speeds has rapidly increased the performance requirements of signature matching. Solutions to meet these requirements range from hardware-centric ASIC/FPGA implementations to software implementations using high-performance microprocessors. In this paper, we propose a programmable signature matching system prototyped on an Nvidia G80 GPU. We first present a detailed architectural and microarchitectural analysis, showing that signature matching is well suited for SIMD processing because of regular control flow and parallelism available at the packet level. Next, we examine two approaches for matching signatures: standard deterministic finite automata (DFAs) and extended finite automata (XFAs), which use far less memory than DFAs but require specialized auxiliary memory and small amounts of computation in most states. We implement a fully functional prototype on the SIMD-based G80 GPU. This system out-performs a Pentium4 by up to 9× and a Niagara-based 32-threaded system by up to 2.3X and shows that GPUs are a promising candidate for signature matching.
机译:现代网络设备采用深度数据包检查,以实现复杂的服务,如入侵检测,流量整形和负载平衡。在此类服务的核心,是一个签名匹配引擎,必须将数据包有效载荷与线速率的多个签名匹配。然而,最近转换到复杂的常规表达式基于基于网络速度的基于网络速度的签名迅速增加了签名匹配的性能要求。解决方案以满足这些要求的范围从以硬件为中心的ASIC / FPGA实现,以使用高性能微处理器的软件实现。在本文中,我们提出了一种可编程签名匹配系统在NVIDIA G80 GPU上的原型。我们首先提供了详细的建筑和微体系结构,表明签名匹配对于SIMD处理非常适合,因为在数据包级别可用的定期控制流程和并行性。接下来,我们检查两种匹配签名方法:标准确定性有限自动机(DFA)和扩展有限自动机(XFAS),它使用的内存远低于DFA,但在大多数状态下需要专门的辅助存储器和少量计算。我们在基于SIMD的G80 GPU上实现了全功能原型。该系统通过高达9次的32个32螺纹系统推出了高达9倍和尼亚加拉的32螺纹系统,并显示GPU是签名匹配的有希望的候选者。

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