{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/parallel-batch-dynamic-k-d-trees","title":"Parallel Batch-Dynamic $k$d-Trees","arxiv_id":"2112.06188","date":"2021-12-12","proceeding":null,"authors":["Rahul Yesantharao","Yiqiu Wang","Laxman Dhulipala","Julian Shun"],"abstract":"$k$d-trees are widely used in parallel databases to support efficient neighborhood/similarity queries. Supporting parallel updates to $k$d-trees is therefore an important operation. In this paper, we present BDL-tree, a parallel, batch-dynamic implementation of a $k$d-tree that allows for efficient parallel $k$-NN queries over dynamically changing point sets. BDL-trees consist of a log-structured set of $k$d-trees which can be used to efficiently insert or delete batches of points in parallel with polylogarithmic depth. Specifically, given a BDL-tree with $n$ points, each batch of $B$ updates takes $O(B\\log^2{(n+B)})$ amortized work and $O(\\log(n+B)\\log\\log{(n+B)})$ depth (parallel time). We provide an optimized multicore implementation of BDL-trees. Our optimizations include parallel cache-oblivious $k$d-tree construction and parallel bloom filter construction. Our experiments on a 36-core machine with two-way hyper-threading using a variety of synthetic and real-world datasets show that our implementation of BDL-tree achieves a self-relative speedup of up to $34.8\\times$ ($28.4\\times$ on average) for batch insertions, up to $35.5\\times$ ($27.2\\times$ on average) for batch deletions, and up to $46.1\\times$ ($40.0\\times$ on average) for $k$-nearest neighbor queries. In addition, it achieves throughputs of up to 14.5 million updates/second for batch-parallel updates and 6.7 million queries/second for $k$-NN queries. We compare to two baseline $k$d-tree implementations and demonstrate that BDL-trees achieve a good tradeoff between the two baseline options for implementing batch updates.","url_abs":"https://arxiv.org/abs/2112.06188v1","url_pdf":"https://arxiv.org/pdf/2112.06188v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"parallel-batch-dynamic-k-d-trees","repo_url":"https://github.com/rahulyesantharao/batch-dynamic-kdtree","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"parallel-batch-dynamic-k-d-trees","repo_url":"https://github.com/paralg/pargeo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}