cuVS

Source: GitHub - rapidsai/cuvs

Category: Open-source

Tags: open-source, gpu-acceleration, vector-search, high-performance

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Description

cuVS is an open-source library from RAPIDS for fast, GPU-accelerated vector search and clustering, designed to simplify and accelerate vector similarity search and clustering tasks using state-of-the-art algorithms on NVIDIA GPUs. It is derived from the RAPIDS RAFT library and will become its primary home for approximate nearest neighbors (ANN) and clustering algorithms in late 2024.

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Features

• GPU-accelerated vector search: High-performance implementations of vector similarity search algorithms on NVIDIA GPUs.
• Approximate Nearest Neighbor (ANN) algorithms: State-of-the-art ANN algorithms for scalable and efficient similarity search in high-dimensional spaces.
• Clustering algorithms: Algorithms for clustering, including support for methods like K-means and integration with algorithms used in t-SNE, UMAP, and HDBSCAN.
• Integration-ready: Can be used directly or integrated into databases and other libraries for fast vector search and clustering.
• Modern CUDA support: Built to leverage the latest NVIDIA architectures and CUDA versions, ensuring optimal performance and scalability.
• Derived from RAPIDS RAFT: Contains the most up-to-date ANN and clustering algorithms migrated from the RAFT library.
• Enables graph construction: Facilitates fast construction of nearest neighbor graphs, which can be used for further graph algorithms and analyses.
• Open-source and actively maintained: The library is open-source and will continue to receive updates and performance improvements as new hardware and CUDA versions are released.
• Multi-language support: Codebase includes support for Python, C++, Java, Go, and Rust bindings or interfaces.
• Documentation and examples: Includes documentation, examples, and notebooks to help users get started.

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Pricing

cuVS is open-source and free to use under its license.

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License

cuVS is released under an open-source license (see the repository for details).