Coordination-Free Lane Partitioning for Convergent ANN Search
Carl Kugblenu, Petri Vuorimaa

TL;DR
This paper introduces a coordination-free lane partitioning method for approximate nearest neighbor search that eliminates redundant candidate rediscovery, significantly improving recall and ranking metrics without increasing computational cost.
Contribution
The authors propose a deterministic, coordination-free lane partitioning technique that enhances candidate diversity and search effectiveness in vector search systems.
Findings
Recall@10 on SIFT1M increases from 0.249 to 0.999
Hit@10 on MS MARCO improves from 0.200 to 0.601
Lane overlap reduces from nearly 100% to 0%
Abstract
Production vector search systems often fan out each query across parallel lanes (threads, replicas, or shards) to meet latency service-level objectives (SLOs). In practice, these lanes rediscover the same candidates, so extra compute does not increase coverage. We present a coordination-free lane partitioner that turns duplication into complementary work at the same cost and deadline. For each query we (1) build a deterministic candidate pool sized to the total top-k budget, (2) apply a per-query pseudorandom permutation, and (3) assign each lane a disjoint slice of positions. Lanes then return different results by construction, with no runtime coordination. At equal cost with four lanes (total candidate budget 64), on SIFT1M (1M SIFT feature vectors) with Hierarchical Navigable Small World graphs (HNSW) recall@10 rises from 0.249 to 0.999 while lane overlap falls from nearly 100% to…
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Taxonomy
TopicsAlgorithms and Data Compression · Information Retrieval and Search Behavior · Advanced Database Systems and Queries
