IP lookup via Longest Prefix Match (LPM) is critical for packet forwarding. Unfortunately, conventional lookup algorithms are inefficient for IPv6 Forwarding Information Bases (FIBs), which are characterized by a set of long prefixes with diverse lengths. We observe that LPM inherently represents a two-dimensional (2D) search problem over both prefix values and prefix lengths, but existing algorithms mostly treat LPM as two separate levels of one-dimensional (1D) searches, causing poor lookup performance and high memory overhead. This paper presents PlanB, a novel scheme for high-speed IPv6 lookup. We transform the 2D LPM into an equivalent 1D search problem over elementary intervals, thereby unifying the search across prefix value and lengths. We then adapt a flat-array-based B-tree structure to the needs of LPM to propose the linearized $B^+$-tree, based on which we introduce an efficient search algorithm tailored to the properties of the transformed space. To maximize performance, we integrate PlanB with vectorization, batching, branch-free logic, and loop unrolling to fully exploit CPU parallelism. Extensive evaluation shows that PlanB achieves single-core performance of 390 Million Lookups Per Sec (MLPS) with real-world IPv6 FIBs on AMD processor, and scales to full-12-core performance of 3.4 Billion Lookups Per Sec (BLPS). This is 1.6$\times$$\sim$14$\times$ higher than state-of-the-art software-based schemes (PopTrie, CP-Trie, Neurotrie and HBS).

翻译：摘要：通过最长前缀匹配（LPM）进行的IP查找对数据包转发至关重要。然而，传统查找算法对于IPv6转发信息库（FIB）效率低下，这类FIB以一组长度多样的长前缀为特征。我们观察到，LPM本质上代表了一个涉及前缀值和前缀长度的二维（2D）搜索问题，但现有算法大多将LPM视为两个分离的一维（1D）搜索层次，导致查找性能低下且内存开销高昂。本文提出PlanB，一种新颖的高速IPv6查找方案。我们将二维LPM转化为等价的一维搜索问题（基于基本区间），从而统一了前缀值和长度的搜索。随后，我们针对LPM需求对基于扁平数组的B树结构进行适配，提出了线性化$B^+$-树，并基于此引入了一种适配变换后空间特性的高效搜索算法。为最大化性能，我们将PlanB与向量化、批处理、无分支逻辑及循环展开相结合，以充分利用CPU并行性。大量评估表明，在AMD处理器上使用真实世界IPv6 FIB时，PlanB实现了单核性能达3.9亿次查找/秒（MLPS），并扩展至全12核性能达34亿次查找/秒（BLPS）。这一性能比现有最先进的软件方案（PopTrie、CP-Trie、Neurotrie和HBS）高出1.6倍至14倍。