Towards the Distributed Large-scale k-NN Graph Construction by Graph Merge
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917208902336512 |
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| author | Zhang, Cheng Zhao, Wan-Lei Xiao, Shihai Yao, Jiajie Zhang, Xuecang |
| author_facet | Zhang, Cheng Zhao, Wan-Lei Xiao, Shihai Yao, Jiajie Zhang, Xuecang |
| contents | In order to support the real-time interaction with LLMs and the instant search or the instant recommendation on social media, it becomes an imminent problem to build a k-NN graph or an indexing graph for the massive number of vectorized multimedia data. In such scenarios, the scale of the data or the scale of the graph may exceed the processing capacity of a single machine. This paper aims to address the graph construction problem of such scale via efficient graph merge. For the graph construction on a single node, two generic and highly parallelizable algorithms, namely Two-way Merge and Multi-way Merge are proposed to merge subgraphs into one. For the graph construction across multiple nodes, a multi-node procedure based on Two-way Merge is presented. The procedure makes it feasible to construct a large-scale k-NN graph/indexing graph on either a single node or multiple nodes when the data size exceeds the memory capacity of one node. Extensive experiments are conducted on both large-scale k-NN graph and indexing graph construction. For the k-NN graph construction, the large-scale and high-quality k-NN graphs are constructed by graph merge in parallel. Typically, a billion-scale k-NN graph can be built in approximately 17h when only three nodes are employed. For the indexing graph construction, similar NN search performance as the original indexing graph is achieved with the merged indexing graphs while requiring much less time of construction. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_11697 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards the Distributed Large-scale k-NN Graph Construction by Graph Merge Zhang, Cheng Zhao, Wan-Lei Xiao, Shihai Yao, Jiajie Zhang, Xuecang Distributed, Parallel, and Cluster Computing In order to support the real-time interaction with LLMs and the instant search or the instant recommendation on social media, it becomes an imminent problem to build a k-NN graph or an indexing graph for the massive number of vectorized multimedia data. In such scenarios, the scale of the data or the scale of the graph may exceed the processing capacity of a single machine. This paper aims to address the graph construction problem of such scale via efficient graph merge. For the graph construction on a single node, two generic and highly parallelizable algorithms, namely Two-way Merge and Multi-way Merge are proposed to merge subgraphs into one. For the graph construction across multiple nodes, a multi-node procedure based on Two-way Merge is presented. The procedure makes it feasible to construct a large-scale k-NN graph/indexing graph on either a single node or multiple nodes when the data size exceeds the memory capacity of one node. Extensive experiments are conducted on both large-scale k-NN graph and indexing graph construction. For the k-NN graph construction, the large-scale and high-quality k-NN graphs are constructed by graph merge in parallel. Typically, a billion-scale k-NN graph can be built in approximately 17h when only three nodes are employed. For the indexing graph construction, similar NN search performance as the original indexing graph is achieved with the merged indexing graphs while requiring much less time of construction. |
| title | Towards the Distributed Large-scale k-NN Graph Construction by Graph Merge |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2509.11697 |