Billion-scale ANNS Benchmarks
A benchmarking resource for evaluating approximate nearest neighbor search (ANNS) methods on billion-scale datasets, highly relevant for assessing the scalability of vector databases.
About this tool
Billion-scale ANNS Benchmarks
A benchmarking framework for evaluating approximate nearest neighbor search (ANNS) algorithms on billion-scale datasets. It is designed to assess the scalability and performance of vector databases and ANNS methods on very large datasets.
Features
- Provides a framework for benchmarking ANNS algorithms at the billion-scale level
- Supports evaluation of both algorithms and hardware for scalability and performance
- Includes datasets suitable for billion-scale benchmarking (details available on the project website)
- Resources and guides for running benchmarks and interpreting results
- Historical results and documentation for NeurIPS 2021 and NeurIPS 2023 competitions
- Tools for dataset preparation, evaluation, and result visualization
- Open-source and extensible for new datasets or algorithms
Category
- Benchmarks & Evaluation
Tags
benchmark, anns, scalability, performance
Source
https://github.com/harsha-simhadri/big-ann-benchmarks
Pricing
- No pricing information provided (open source project).
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