Towards a GPU accelerated virtual machine for massively parallel packet classification and filtering

This paper considers the application of GPU co-processors to accelerate the analysis of packet data, particularly within extremely large packet traces spanning months or years of traffic. Discussion focuses on the construction, performance and limitations of the experimental GPF (GPU Packet Filter), which employs a prototype massively-parallel protocol-independent multi-match algorithm to rapidly compare packets against multiple arbitrary filters. The paper concludes with a consideration of mechanisms to expand the flexibility and power of the GPF algorithm to construct a fully programmable GPU packet classification virtual machine, which can perform massively parallel classification, data-mining and data-transformation to explore and analyse packet traces. This virtual machine is a component of a larger framework of capture analysis tools which together provide capture indexing, manipulation, filtering and visualisation functions