Optimizing Many-field Packet Classification on FPGA, Multi-core General Purpose Processor, and GPU

Abstract

Due to the rapid growth of Internet, there is an increasing need for efficiently classifying packets with many header fields in large rule sets. For example, in Software Defined Networking (SDN), the OpenFlow table lookup can require 15 packet header fields to be examined. In this paper, we present several decomposition-based packet classification implementations with efficient optimization techniques. In the searching phase, packet headers are split or combined. In the merging phase, the partial searching results from all the fields are merged to generate the final result. We prototype our implementations on state-of-the-art Field Programmable Gate Array (FPGA), multi-core General Purpose Processor (GPP), and Graphics Processing Unit (GPU). On FPGA, we propose two optimization techniques to divide generic ranges; modular processing elements are constructed and concatenated into a systolic array. On multi-core GPP, we parallelize both the searching and merging phases using parallel program threads. On the GPU-accelerated platform, we minimize branch divergence and reduce the data communication overhead. Experimental results show that 500 Million Packets Per Second (MPPS) throughput and 3 us latency can be achieved for 1.5 K rule sets on FPGA. We achieve 14.7 MPPS throughput and 30.5 MPPS throughput for 32 K rule sets on multi-core GPP and GPU-accelerated platforms, respectively. As a heterogeneous solution, our GPU-accelerated packet classifier shows 2X speedup compared to the implementation using multi-core GPP only. Compared with prior works, our designs can match long packet headers against very complex rule sets.