pith:FCAPCZUO
To GPU or Not to GPU: Vector Search in Relational Engines
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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Claims
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.
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.
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.
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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
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· · · · ·Agent API
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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