Speed-ANN：低延迟高精度的查询内搜索最近邻算法
Speed-ANN: Low-Latency and High-Accuracy Nearest Neighbor Search via Intra-Query Parallelism
摘要：
最近邻搜索（NNS）由于其在数据科学和人工智能应用中管理高维向量数据的核心作用，最近引起了人们的兴趣。神经嵌入的成功激发了人们的兴趣，其中深度学习模型将非结构化数据转换为语义相关的特征向量以进行数据分析，例如，推荐热门商品。在快速神经网络分类的几类方法中，相似图是最成功的算法趋势之一。几个最流行和性能最好的相似性图，如NSG和HNSW，在其核心采用最佳优先遍历沿着底层图索引搜索近邻。最大化搜索性能对于许多任务来说都是至关重要的，特别是在大规模和高召回率的情况下。在这项工作中，我们提供了最先进的相似性搜索算法的挑战的深入研究，揭示了它的挑战，利用多核处理器，以加快搜索效率。我们还利用是否相似图搜索是强大的偏离保持严格的顺序，允许多个步行者同时推进搜索前沿。基于我们的见解，我们提出了速度人工神经网络，一个并行的相似性搜索算法，利用隐藏的查询内并行性和存储层次结构，允许相似性搜索，以利用多个CPU内核，显着加快搜索速度，同时实现高精度。我们在各种数据集上评估了Speed-ANN，范围从数百万到数十亿个数据点，并分别显示其比NSG和HNSW更短的查询延迟。此外，在多核支持下，我们表明我们的方法比高度优化的GPU实现提供更快的搜索延迟，并且随着硬件资源数量的增加（例如，CPU核心）和图形大小。
英文摘要：
Nearest Neighbor Search (NNS) has recently drawn a rapid increase of interest due to its core role in managing high-dimensional vector data in data science and AI applications. The interest is fueled by the success of neural embedding, where deep learning models transform unstructured data into semantically correlated feature vectors for data analysis, e.g., recommend popular items. Among several categories of methods for fast NNS, similarity graph is one of the most successful algorithmic trends. Several of the most popular and top-performing similarity graphs, such as NSG and HNSW, at their core employ best-first traversal along the underlying graph indices to search near neighbors. Maximizing the performance of the search is essential for many tasks, especially at the large-scale and high-recall regime. In this work, we provide an in-depth examination of the challenges of the state-of-the-art similarity search algorithms, revealing its challenges in leveraging multi-core processors to speed up the search efficiency. We also exploit whether similarity graph search is robust to deviation from maintaining strict order by allowing multiple walkers to simultaneously advance the search frontier. Based on our insights, we propose Speed-ANN, a parallel similarity search algorithm that exploits hidden intra-query parallelism and memory hierarchy that allows similarity search to take advantage of multiple CPU cores to significantly accelerate search speed while achieving high accuracy. We evaluate Speed-ANN on a wide range of datasets, ranging from million to billion data points, and show its shorter query latency than NSG and HNSW, respectively. Besides, with multicore support, we show that our approach offers faster search latency than highly-optimized GPU implementation and provides good scalability as the increase of the number of hardware resources (e.g., CPU cores) and graph sizes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
