Inside FAISS: Billion-Scale Similarity Search
a day ago
- #vector-search
- #FAISS
- #nearest-neighbor
- Everything in AI—images, text, audio—is converted into vectors (embeddings) that capture semantic meaning in a high-dimensional space, where similarity is measured by distance.
- Scalable nearest-neighbor search is challenging: brute force is impractical for billions of vectors due to high computational and memory costs (e.g., 512 GB for 1 billion SIFT descriptors).
- FAISS enables efficient approximate search by combining partitioning (IVF) to skip irrelevant vectors and compression (Product Quantization) to reduce memory usage, making billion-scale search feasible.
- Product Quantization splits vectors into sub-vectors, quantizes each with a small codebook, and compresses them to as little as 8 bytes, achieving 64× memory reduction while preserving meaningful distance approximations.
- IVFPQ combines IVF and PQ for high performance: IVF prunes search to relevant clusters, and PQ encodes residuals (vector offsets) for efficient distance calculations via lookup tables (ADC method).
- GPU acceleration of IVFPQ uses coalesced reads, shared-memory LUTs, and WarpSelect to achieve microsecond-level query times on billion-vector datasets, optimizing memory bandwidth and parallelism.
- FAISS powers real-world applications like semantic search, image similarity, RAG for LLMs, recommendation systems, document deduplication, and anomaly detection by enabling fast vector retrieval.