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RedNote's HELMSMAN cuts ANNS hardware costs by 90%

By PulseAugur Editorial · [1 sources] ·

Researchers at RedNote (Xiaohongshu) have developed HELMSMAN, a new clustering-based approximate nearest neighbor search (ANNS) system designed to significantly reduce hardware costs for large-scale ANNS deployments. By integrating a userspace storage stack, a learned pruning module, and GPU-accelerated construction pipelines, HELMSMAN achieves substantial savings, reducing hardware costs by over 90%. The system can handle billion-scale index rebuilds within hours and currently supports ANNS workloads on 40 machines that previously required approximately 35,000 cores and 0.35 PB of DRAM. AI

IMPACT Reduces hardware costs for large-scale ANNS, potentially enabling wider adoption of AI-powered search and recommendation systems.

RANK_REASON Academic paper detailing a new system for ANNS. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RedNote's HELMSMAN cuts ANNS hardware costs by 90%

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Academic paper detailing a new system for ANNS. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Chuliang Weng ·

    The Clustering Strikes Back: Building Cost-Effective and High-Performance ANNS at Scale with Helmsman

    RedNote (a.k.a., Xiaohongshu, a global-scale social network platform) widely adopts approximate nearest neighbor search (ANNS) to power its search, recommendation, and advertising services. Due to the demanding Service Level Agreements (SLAs), we have to rely on in-memory graph-b…

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