Spider: Parallelizing Longest Prefix Matching with Optimization for SIMD Instructions

Ueno, Yukito; Nakamura, Ryô; Kuga, Yohei; Esaki, Hiroshi

doi:10.1109/netsoft48620.2020.9165306

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…This work introduces new contributions in three main points, although our previous work [14] has already proposed the basic concept of Spider. First, we provide a more detailed explanation of the concept of Spider ( § III).…”

Section: Introductionmentioning

confidence: 99%

Fast Longest Prefix Matching by Exploiting SIMD Instructions

et al. 2020

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Longest prefix matching (LPM) is a fundamental process in IP routing used not only in traditional hardware routers but also in software middleboxes. However, the performance of LPM in software is still insufficient for processing packets at over 100 Gbps, although previous studies have tackled this issue by exploiting the CPU cache or accelerators such as GPUs. To improve the performance of software LPM further, we propose a novel LPM method called Spider, which exploits a single-instruction multiple-data (SIMD) mechanism in the CPU. Spider achieves performing LPM for up to 16 destination IP address in parallel by a routing table structure carefully designed for processing by the SIMD instructions. We evaluated Spider from the following three perspectives: the improvement of LPM performance derived from the parallelism provided by the SIMD mechanism, performance comparison with other methods, and performance scalability. The evaluation shows that Spider dramatically improves the LPM performance, which reaches 1.8-3.2 times compared with the state-of-the-art methods. Moreover, Spider achieves 5,074 million lookups per second with 16 CPU cores, which is equivalent to the processing capacity of 3.4 Tbps in short packets; the performance opens up the possibility of packet processing at the terabit-class rate by software.INDEX TERMS IP routing, longest prefix matching (LPM), single-instruction multiple-data (SIMD), software middlebox.

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Section: Introductionmentioning

confidence: 99%

Fast Longest Prefix Matching by Exploiting SIMD Instructions

et al. 2020

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Feasibility of Longest Prefix Matching using Learned Index Structures

Higuchi

Takemasa

Koizumi

et al. 2021

SIGMETRICS Perform. Eval. Rev.

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This paper revisits longest prefix matching in IP packet forwarding because an emerging data structure, learned index, is recently presented. A learned index uses machine learning to associate key-value pairs in a key-value store. The fundamental idea to apply a learned index to an FIB is to simplify the complex longest prefix matching operation to a nearest address search operation. The size of the proposed FIB is less than half of an existing trie-based FIB while it achieves the computation speed nearly equal to the trie-based FIB. Moreover, the computation speed of the proposal is independent of the length of IP prefixes, unlike trie-based FIBs.

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