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pith:2026:FCAPCZUOBLQZO2FYJLUBLJHU4Q
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To GPU or Not to GPU: Vector Search in Relational Engines

Bowen Wu, Gustavo Alonso, Joel Andr\'e, Marko Kabi\'c, Vasilis Mageirakos, Yannis Chronis

An alternative organization of vector indexes and embeddings lets GPUs accelerate both relational queries and vector search in database engines.

arxiv:2605.15957 v1 · 2026-05-15 · cs.DB

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4 Citations open
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Claims

C1strongest claim

With an alternative organization of vector index and embeddings that reduces index size, both the relational and vector search components are faster on the GPU, particularly on fast interconnects, in contrast with the architecture used in existing engines.

C2weakest assumption

The modular execution engine developed for the experiments accurately models the overheads and integration costs that would appear in a production relational database engine when adding GPU vector search support.

C3one line summary

Relational engines achieve faster SQL+vector-search queries on GPU than CPU when using compact vector indexes and fast interconnects, reversing the CPU-only design in current systems.

References

65 extracted · 65 resolved · 3 Pith anchors

[1] DuckDB Vector Similarity Search (VSS) Extension 2024
[2] Apache Software Foundation. 2026. Apache Arrow: A Cross-Language Devel- opment Platform for In-Memory Data. https://arrow.apache.org/. Accessed: 12 2026-04-29 2026
[3] Felipe Aramburú, William Malpica, Kaouther Abrougui, Amin Aramoon, Ro- mulo Auccapuclla, Claude Brisson, Matthijs Brobbel, Colby Farrell, Pradeep Garigipati, Joost Hoozemans, et al. 2025. Theseus: A D 2025
[4] Martin Aumüller, Erik Bernhardsson, and Alexander Faithfull. 2020. ANN- Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms. Information Systems87 (2020), 101374. https://doi.o 2020 · doi:10.1016/j.is.2019.02.006
[5] David Boehme, Todd Gamblin, David Beckingsale, Peer-Timo Bremer, Alfredo Gimenez, Matthew LeGendre, Olga Pearce, and Martin Schulz. 2016. Caliper: performance introspection for HPC software stacks. In 2016

Formal links

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Receipt and verification
First computed 2026-05-20T00:01:46.610061Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

2880f1668e0ae19768b84ae815a4f4e43f3fbdd86b4a971c7b65902a512663af

Aliases

arxiv: 2605.15957 · arxiv_version: 2605.15957v1 · doi: 10.48550/arxiv.2605.15957 · pith_short_12: FCAPCZUOBLQZ · pith_short_16: FCAPCZUOBLQZO2FY · pith_short_8: FCAPCZUO
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FCAPCZUOBLQZO2FYJLUBLJHU4Q \
  | 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: 2880f1668e0ae19768b84ae815a4f4e43f3fbdd86b4a971c7b65902a512663af
Canonical record JSON
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