GPU-accelerated vector search for AMD GPUs

hipVS is a port of the RAPIDS cuvs library. It aims to follow the latter's directory structure, file naming and API naming as closely as possible to minimize porting friction for users that are interested in using both projects.

It contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of hipVS is to simplify the use of GPUs for vector similarity search and clustering.

Vector search is an information retrieval method that has been growing in popularity over the past few years, partly because of the rising importance of multimedia embeddings created from unstructured data and the need to perform semantic search on the embeddings to find items which are semantically similar to each other.

Vector search is also used in data mining and machine learning tasks and comprises an important step in many clustering and visualization algorithms like UMAP, t-SNE, K-means, and HDBSCAN.

Finally, faster vector search enables interactions between dense vectors and graphs. Converting a pile of dense vectors into nearest neighbors graphs unlocks the entire world of graph analysis algorithms, such as those found in GraphBLAS.

Below are some common use-cases for vector search:

• Semantic search

  • Generative AI & Retrieval augmented generation (RAG)
  • Recommender systems
  • Computer vision
  • Image search
  • Text search
  • Audio search
  • Molecular search
  • Model training

• Data mining

  • Clustering algorithms
  • Visualization algorithms
  • Sampling algorithms
  • Class balancing
  • Ensemble methods
  • k-NN graph construction

There are several benefits to using hipVS and GPUs for vector search, including

1. Fast index build
2. Latency critical and high throughput search
3. Parameter tuning
4. Cost savings
5. Interoperability (build on GPU, deploy on CPU)
6. Multiple language support
7. Building blocks for composing new or accelerating existing algorithms

• Approximate & exact nearest-neighbor search – HNSW, IVF-PQ, brute force and more
• C++, C & Python interfaces – integrate in low-latency services or rapid prototyping notebooks
• API-compatible with cuVS – drop-in replacement for existing cuVS workflows on AMD hardware

• The library is currently only tested on AMD GPUs with the ROCm stack.
• Multi-GPU and Multi Node functionality is still experimental and not officially supported.

For the interested reader, many of the accelerated implementations in hipVS are also based on research papers which can provide a lot more background. We also ask you to please cite the corresponding algorithms by referencing them in your own research.

• CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search
• Top-K Algorithms on GPU: A Comprehensive Study and New Methods
• Fast K-NN Graph Construction by GPU Based NN-Descent
• cuSLINK: Single-linkage Agglomerative Clustering on the GPU
• GPU Semiring Primitives for Sparse Neighborhood Methods