Pith Number

pith:V7VZMQQJ

pith:2026:V7VZMQQJPBUSD7TAVMQ2LTVV53

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AlayaLaser: Efficient Index Layout and Search Strategy for Large-scale High-dimensional Vector Similarity Search

Bo Tang, Gezi Li, Haotian Liu, Liang Huang, Long Xiang, Weijian Chen, Yangshen Deng

AlayaLaser shows that on-disk graph indexes for high-dimensional vectors can match or beat in-memory speed by fixing compute bottlenecks instead of chasing I/O savings.

arxiv:2602.23342 v2 · 2026-02-26 · cs.DB · cs.IR

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Claims

C1strongest claim

AlayaLaser not only surpasses existing on-disk graph-based index systems but also matches or even exceeds the performance of in-memory index systems.

C2weakest assumption

That the performance bottleneck of existing on-disk graph-based systems is primarily compute-bound rather than I/O-bound once dimensionality reaches hundreds or thousands, and that the proposed SIMD-friendly layout plus heuristics will reliably translate this insight into measurable gains across datasets.

C3one line summary

AlayaLaser uses a SIMD-optimized on-disk graph layout plus caching and search strategies to outperform prior on-disk ANNS systems and match or exceed in-memory performance on large high-dimensional datasets.

References

58 extracted · 58 resolved · 2 Pith anchors

[1] Cecilia Aguerrebere, Ishwar Bhati, Mark Hildebrand, Mariano Tepper, and Ted Willke. 2023. Similarity search in the blink of an eye with compressed indices. arXiv preprint arXiv:2304.04759(2023) 2023

[2] Fabien André, Anne-Marie Kermarrec, and Nicolas Le Scouarnec. 2017. Acceler- ated nearest neighbor search with quick adc. InICMR. 159–166 2017

[3] Fabien André, Anne-Marie Kermarrec, and Nicolas Le Scouarnec. 2019. Quicker adc: Unlocking the hidden potential of product quantization with simd.TPAMI 43, 5 (2019), 1666–1677 2019

[4] Argilla. 2024. PersonaHub-FineWeb-Edu-4-Embeddings. Hugging Face dataset.https://huggingface.co/datasets/argilla-warehouse/personahub- fineweb-edu-4-embeddingsAccessed: 2025-10-06 2024

[5] Martin Aumüller, Erik Bernhardsson, and Alexander Faithfull. 2020. ANN- Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Information Systems87 (2020), 101374 2020

Cited by

2 papers in Pith

PipeANN-Filter: An Efficient Filtered Vector Search System on SSD

Low-Latency Out-of-Core ANN Search in High-Dimensional Space

Receipt and verification

First computed	2026-05-17T23:39:15.924874Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

afeb964209786921fe60ab21a5ceb5eed54898aa93884d87e30d349995924cd4

Aliases

arxiv: 2602.23342 · arxiv_version: 2602.23342v2 · doi: 10.48550/arxiv.2602.23342 · pith_short_12: V7VZMQQJPBUS · pith_short_16: V7VZMQQJPBUSD7TA · pith_short_8: V7VZMQQJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/V7VZMQQJPBUSD7TAVMQ2LTVV53 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: afeb964209786921fe60ab21a5ceb5eed54898aa93884d87e30d349995924cd4

Canonical record JSON

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    "abstract_canon_sha256": "71ad4d3936bcb397c32c25e0124621a37602019a59bee0d55661e850956e2c60",
    "cross_cats_sorted": [
      "cs.IR"
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    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "cs.DB",
    "submitted_at": "2026-02-26T18:48:29Z",
    "title_canon_sha256": "bcf48dc815aace3d2c369f80d0816c0e58f2fc24f40f5308e715657278a127a6"
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    "kind": "arxiv",
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