Evaluating GPUs for network packet signature matching

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 hardwarecentric 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 9X and a Niagara-based 32-threaded system by up to 2.3X and shows that GPUs are a promising candidate for signature matching.