基于图重排的高速缓存高效近邻搜索
Graph Reordering for Cache-Efficient Near Neighbor Search
摘要：
图搜索是最近邻搜索中最成功的算法趋势之一。几个最流行和经验上最成功的算法，在其核心，是一个简单的沿着修剪近邻图行走。这些算法始终在嵌入式搜索等应用的工业速度基准中名列前茅。然而，图遍历应用程序经常遭受不良的内存访问模式，近邻搜索也不例外。我们的测量结果表明，流行的搜索索引，如分层导航小世界图（HNSW）可能有缓存未命中性能差。为了解决这个问题，我们应用图重排算法的近邻图。图重新排序是一种内存布局优化，它将内存中经常访问的节点分组在一起。我们提出了详尽的实验应用几种重排序算法的领先的基于图形的近邻方法的基础上HNSW指数。我们发现，重新排序提高了查询时间高达40%，我们证明，重新排序的图所需的时间是可以忽略不计相比，所需的时间来构建索引。
英文摘要：
Graph search is one of the most successful algorithmic trends in near neighbor search. Several of the most popular and empirically successful algorithms are, at their core, a simple walk along a pruned near neighbor graph. Such algorithms consistently perform at the top of industrial speed benchmarks for applications such as embedding search. However, graph traversal applications often suffer from poor memory access patterns, and near neighbor search is no exception to this rule. Our measurements show that popular search indices such as the hierarchical navigable small-world graph (HNSW) can have poor cache miss performance. To address this problem, we apply graph reordering algorithms to near neighbor graphs. Graph reordering is a memory layout optimization that groups commonly-accessed nodes together in memory. We present exhaustive experiments applying several reordering algorithms to a leading graph-based near neighbor method based on the HNSW index. We find that reordering improves the query time by up to 40%, and we demonstrate that the time needed to reorder the graph is negligible compared to the time required to construct the index.
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