{"ID":2845719,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.03298","arxiv_id":"2511.03298","title":"KScaNN: Scalable Approximate Nearest Neighbor Search on Kunpeng","abstract":"Approximate Nearest Neighbor Search (ANNS) is a cornerstone algorithm for information retrieval, recommendation systems, and machine learning applications. While x86-based architectures have historically dominated this domain, the increasing adoption of ARM-based servers in industry presents a critical need for ANNS solutions optimized on ARM architectures. A naive port of existing x86 ANNS algorithms to ARM platforms results in a substantial performance deficit, failing to leverage the unique capabilities of the underlying hardware. To address this challenge, we introduce KScaNN, a novel ANNS algorithm co-designed for the Kunpeng 920 ARM architecture. KScaNN embodies a holistic approach that synergizes sophisticated, data aware algorithmic refinements with carefully-designed hardware specific optimizations. Its core contributions include: 1) novel algorithmic techniques, including a hybrid intra-cluster search strategy and an improved PQ residual calculation method, which optimize the search process at a higher level; 2) an ML-driven adaptive search module that provides adaptive, per-query tuning of search parameters, eliminating the inefficiencies of static configurations; and 3) highly-optimized SIMD kernels for ARM that maximize hardware utilization for the critical distance computation workloads. The experimental results demonstrate that KScaNN not only closes the performance gap but establishes a new standard, achieving up to a 1.63x speedup over the fastest x86-based solution. This work provides a definitive blueprint for achieving leadership-class performance for vector search on modern ARM architectures and underscores","short_abstract":"Approximate Nearest Neighbor Search (ANNS) is a cornerstone algorithm for information retrieval, recommendation systems, and machine learning applications. While x86-based architectures have historically dominated this domain, the increasing adoption of ARM-based servers in industry presents a critical need for ANNS so...","url_abs":"https://arxiv.org/abs/2511.03298","url_pdf":"https://arxiv.org/pdf/2511.03298v2","authors":"[\"Oleg Senkevich\",\"Siyang Xu\",\"Tianyi Jiang\",\"Alexander Radionov\",\"Jan Tabaszewski\",\"Dmitriy Malyshev\",\"Zijian Li\",\"Daihao Xue\",\"Licheng Yu\",\"Weidi Zeng\",\"Meiling Wang\",\"Xin Yao\",\"Siyu Huang\",\"Gleb Neshchetkin\",\"Qiuling Pan\",\"Yaoyao Fu\"]","published":"2025-11-05T09:01:32Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[]","has_code":false}