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CoWalk method speeds up batch vector queries on disk-resident graphs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CoWalk, a new method for processing batches of vector queries on disk-resident graphs. CoWalk optimizes this process by having multiple queries cooperate on a single thread, reading each required page only once per batch. This cooperative approach significantly reduces I/O operations and speeds up query processing, achieving 1.6 to 1.9 times faster batch execution at high recall levels compared to per-query baselines. AI

IMPACT This research could improve the efficiency of AI systems that rely on batch processing of vector queries for tasks like research and policy optimization.

RANK_REASON The cluster contains a research paper detailing a new method for processing graph queries. [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 →

CoWalk method speeds up batch vector queries on disk-resident graphs

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The cluster contains a research paper detailing a new method for processing graph queries. [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) · Nong Xiao ·

    CoWalk: Cooperative Walks for Agentic Batch Queries on Disk-Resident Graphs

    Workloads such as query rewriting, deep research and group-relative policy optimization (GRPO) issue batches of semantically related vector queries. Existing disk-resident approximate nearest neighbor (ANN) systems process these queries independently. On three workloads, 31 to 67…

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