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Performance Analysis of Multi-dimensional Packet Classification on Programmable Network Processors
Tampa, Florida, USA November 16-November 18
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/LCN.2004.9429th Annual IEEE International Confer ...
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Deepa Srinivasan, IBM Corporation, RTP, North Carolina, USA
Wu-chang Feng, Portland State University, Oregon, USA
Multi-field packet classification is frequently performed by network devices such as edge routers and firewalls - such devices can utilize programmable network processors to perform this compute-intensive task at nearly line speeds. The architectures of programmable network processors are typically highly parallel and a single algorithm can be apped in different ways onto the hardware. In this paper, we study the performance of two different design mappings of the Bit Vector packet classification algorithm on the Intel? IXP1200 network processor. We show that: (i) Overall, the parallel mapping has better packet processing rate (25% more) than the pipelined mapping; (ii) In the parallel mapping, a processing element's utilization can be considerably affected by code complexity, in terms of branching, because of significant time wasted (as much as 40% more) due to aborting instruction execution pipelines; (iii) In the pipelined mapping, multiple memory reads per packet can lower the overall performance.
Citation:
Deepa Srinivasan, Wu-chang Feng, "Performance Analysis of Multi-dimensional Packet Classification on Programmable Network Processors," lcn, pp.360-367, 29th Annual IEEE International Conference on Local Computer Networks (LCN'04), 2004
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