faiss-quickeradc

Category: vector-database-extensions
Repository: https://github.com/technicolor-research/faiss-quickeradc
Vendor/Brand: technicolor-research

Overview

faiss-quickeradc is an extension of Facebook AI’s FAISS library that integrates the Quicker ADC method to accelerate product-quantization-based approximate nearest neighbor (ANN) search. It focuses on using SIMD (Single Instruction, Multiple Data) shuffle instructions to speed up distance computations in vector database and similarity search workloads.

Features

• FAISS integration: Built as an extension on top of FAISS, preserving its indexing and search APIs while adding faster product quantization routines.
• Quicker ADC implementation: Implements the Quicker Asymmetric Distance Computation (ADC) technique to accelerate PQ-based ANN search.
• SIMD-optimized distance computation: Uses SIMD shuffle instructions to optimize inner loops for product quantization distance calculations.
• Product quantization acceleration: Targets FAISS’s PQ and related index types where ADC is the bottleneck, improving query throughput and latency.
• C and C++ core implementation: Core logic is implemented in C/C++ (as reflected by c_api, gpu, benchs, and core source directories).
• C API bindings: Provides a C API layer (c_api directory) that enables integration from C and other languages that use C FFI.
• Python bindings: Python package bindings (python directory) for using QuickerADC-accelerated indices from Python-based applications and data science workflows.
• GPU components: gpu directory suggests support or integration with FAISS’s GPU stack for ANN search (details and exact coverage should be checked in the repo docs).
• Benchmarking tools: benchs directory with benchmarking utilities to evaluate performance improvements against baseline FAISS implementations.
• Documentation and tutorials: docs and tutorial directories indicating written documentation, examples, and step-by-step guides for building and using the extension.
• Demos and examples: demos and example_makefiles to help users run sample workloads and integrate the library into build systems.
• Testing suite: tests directory providing automated tests for correctness and stability.
• Build system support: Includes cmake, acinclude, and build-aux for building on various platforms and configurations.
• Docker integration: .dockerignore suggests Docker-based workflows are supported or facilitated.
• GitHub CI configuration: .github and .travis.yml for continuous integration and automated builds/tests.

Typical Use Cases

• Accelerating FAISS-based vector search backends in recommendation systems, semantic search, and similarity search services.
• Improving performance of large-scale vector databases that rely on product quantization for memory efficiency.
• Research and experimentation with SIMD-optimized ANN algorithms.

Licensing

• A license file is indicated under the repository’s License section on GitHub. Exact terms (e.g., MIT, BSD, Apache, etc.) should be verified directly in the repository.

Pricing

• faiss-quickeradc is an open-source project hosted on GitHub.
• No paid pricing plans or commercial tiers are indicated in the provided content.