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Reconfigurable Technol. Syst."],"published-print":{"date-parts":[[2024,9,30]]},"abstract":"<jats:p>\n            Due to the high complexity of constructing exact\n            <jats:italic>k<\/jats:italic>\n            -nearest neighbor graphs, approximate construction has become a popular research topic. The NN-Descent algorithm is one of the representative in-memory algorithms. To effectively handle large datasets, existing state-of-the-art solutions combine the divide-and-conquer approach and the NN-Descent algorithm, where large datasets are divided into multiple partitions, and a subgraph is constructed for each partition before all the subgraphs are merged, reducing the memory pressure significantly. However, such solutions fail to address inefficiencies in large-scale\n            <jats:italic>k<\/jats:italic>\n            -nearest neighbor graph construction. In this paper, we propose L-FNNG, a novel solution for accelerating large-scale\n            <jats:italic>k<\/jats:italic>\n            -nearest neighbor graph construction on CPU-FPGA heterogeneous platform. The CPU is responsible for dividing data and determining the order of partition processing, while the FPGA executes all construction tasks to utilize the acceleration capability fully. To accelerate the execution of construction tasks, we design an efficient FPGA accelerator, which includes the\n            <jats:italic>Block-based Scheduling<\/jats:italic>\n            (BS) and\n            <jats:italic>Useless Computation Aborting<\/jats:italic>\n            (UCA) techniques to address the problems of memory access and computation in the NN-Descent algorithm. We also propose an efficient scheduling strategy that includes a KD-tree-based data partitioning method and a hierarchical processing method to address scheduling inefficiency. We evaluate L-FNNG on a Xilinx Alveo U280 board hosted by a 64-core Xeon server. On multiple large-scale datasets, L-FNNG achieves, on average, 2.3\u00d7 construction speedup over the state-of-the-art GPU-based solution.\n          <\/jats:p>","DOI":"10.1145\/3652609","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T12:24:07Z","timestamp":1710419047000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["L-FNNG: Accelerating Large-Scale KNN Graph Construction on CPU-FPGA Heterogeneous Platform"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2180-5428","authenticated-orcid":false,"given":"Chaoqiang","family":"Liu","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6302-813X","authenticated-orcid":false,"given":"Xiaofei","family":"Liao","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7903-2061","authenticated-orcid":false,"given":"Long","family":"Zheng","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3927-1102","authenticated-orcid":false,"given":"Yu","family":"Huang","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China and Zhejiang Lab, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3319-254X","authenticated-orcid":false,"given":"Haifeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1845-0160","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4662-1777","authenticated-orcid":false,"given":"Haiheng","family":"He","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4819-9693","authenticated-orcid":false,"given":"Haoyan","family":"Huang","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9583-198X","authenticated-orcid":false,"given":"Jingyi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3934-7605","authenticated-orcid":false,"given":"Hai","family":"Jin","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}]}],"member":"320","published-online":{"date-parts":[[2024,9,17]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"2055","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201916)","author":"Babenko Artem","year":"2016","unstructured":"Artem Babenko and Victor S. 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