KScaNN: Scalable Approximate Nearest Neighbor Search on Kunpeng
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915934284808192 |
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| author | Senkevich, Oleg Xu, Siyang Jiang, Tianyi Radionov, Alexander Tabaszewski, Jan Malyshev, Dmitriy Li, Zijian Xue, Daihao Yu, Licheng Zeng, Weidi Wang, Meiling Yao, Xin Huang, Siyu Neshchetkin, Gleb Pan, Qiuling Fu, Yaoyao |
| author_facet | Senkevich, Oleg Xu, Siyang Jiang, Tianyi Radionov, Alexander Tabaszewski, Jan Malyshev, Dmitriy Li, Zijian Xue, Daihao Yu, Licheng Zeng, Weidi Wang, Meiling Yao, Xin Huang, Siyu Neshchetkin, Gleb Pan, Qiuling Fu, Yaoyao |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KScaNN: Scalable Approximate Nearest Neighbor Search on Kunpeng Senkevich, Oleg Xu, Siyang Jiang, Tianyi Radionov, Alexander Tabaszewski, Jan Malyshev, Dmitriy Li, Zijian Xue, Daihao Yu, Licheng Zeng, Weidi Wang, Meiling Yao, Xin Huang, Siyu Neshchetkin, Gleb Pan, Qiuling Fu, Yaoyao Information Retrieval 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 |
| title | KScaNN: Scalable Approximate Nearest Neighbor Search on Kunpeng |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2511.03298 |