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Performance analysis of multi-dimensional packet classification on programmable network processors

机译:可编程网络处理器上多维数据包分类的性能分析

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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 mapped 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.
机译:多字段数据包分类通常由诸如边缘路由器和防火墙之类的网络设备执行,此类设备可以利用可编程网络处理器以接近线速的速度执行此计算密集型任务。可编程网络处理器的体系结构通常高度并行,并且单个算法可以以不同的方式映射到硬件上。在本文中,我们研究了英特尔®IXP1200网络处理器上位向量数据包分类算法的两种不同设计映射的性能。我们显示:(ⅰ)总的来说,并行映射比流水线映射具有更好的数据包处理率(多25%); (ⅱ)在并行映射中,由于中止指令执行流水线而浪费大量时间(多达40%以上),因此在分支方面,代码复杂性会大大影响处理元素的利用率。 (ⅲ)在流水线映射中,每个数据包读取多个内存会降低整体性能。

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