RVANNS: Mixed-Precision Indexing and Locality-Aware Graph Traversal on RISC-V

Chengying Huan; Yudong Liu; Jianguo Wang; Lizheng Chen; Renling Yin; Weijia Chen; Ji Qi; Jiageng Yu; Junjie Xu; Jie Zhang; Chen Tian; Yanjun Wu

arXiv · 2608.09077

RVANNS: Mixed-Precision Indexing and Locality-Aware Graph Traversal on RISC-V

Chengying Huan·Yudong Liu·Jianguo Wang·Lizheng Chen·Renling Yin·Weijia Chen·Ji Qi·Jiageng Yu·Junjie Xu·Jie Zhang·Chen Tian·Yanjun Wu

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Abstract

Approximate nearest neighbor search (ANNS) on CPUs is increasingly constrained by candidate-vector movement and decoding rather than peak arithmetic throughput. Although the RISC-V Vector Extension (RVV) provides vector-length-agnostic execution and LMUL-based register grouping, generic low-precision decoding still incurs conversion overhead, while irregular graph traversal generates scattered accesses that degrade cache locality and memory-level parallelism. We present RVANNS, an RVV-oriented ANNS engine that jointly optimizes vector representation and graph locality. Its Mixed-Precision Multi-Layer Index (MPMI) represents each vector with a dense 8-bit affine base and sparse FP16/FP32 residuals, fusing reconstruction with distance accumulation and aligning widening with LMUL-sized register groups. ROrder co-locates likely co-visited graph nodes and sorts remapped adjacency lists, transforming scattered payload probes into denser, predominantly forward-moving address streams. Integrated into Milvus, RVANNS achieves 3.39x and 4.94x speedups over scalar execution on real 128-bit and 256-bit RVV processors, respectively. Under controlled HNSW configurations, it improves throughput by 2.27--2.76x over RVV SIMD+FP32 and by 1.18--1.59x over the corresponding AVX-512 and SVE baselines. On Cohere10M, it further delivers 1.82--2.27x higher QPS/W than the evaluated GPU baselines.

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Chengying Huan, Yudong Liu, Jianguo Wang, Lizheng Chen, Renling Yin, Weijia Chen, Ji Qi, Jiageng Yu, Junjie Xu, Jie Zhang, Chen Tian, Yanjun Wu. 2026-08-10. RVANNS: Mixed-Precision Indexing and Locality-Aware Graph Traversal on RISC-V. https://arxiv.org/abs/2608.09077

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