faiss-quickeradc
faiss-quickeradc is an extension of FAISS that implements the Quicker ADC approach to accelerate product-quantization-based approximate nearest neighbor search using SIMD, improving performance in vector database retrieval.
About this tool
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_apidirectory) that enables integration from C and other languages that use C FFI. - Python bindings: Python package bindings (
pythondirectory) for using QuickerADC-accelerated indices from Python-based applications and data science workflows. - GPU components:
gpudirectory 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:
benchsdirectory with benchmarking utilities to evaluate performance improvements against baseline FAISS implementations. - Documentation and tutorials:
docsandtutorialdirectories indicating written documentation, examples, and step-by-step guides for building and using the extension. - Demos and examples:
demosandexample_makefilesto help users run sample workloads and integrate the library into build systems. - Testing suite:
testsdirectory providing automated tests for correctness and stability. - Build system support: Includes
cmake,acinclude, andbuild-auxfor building on various platforms and configurations. - Docker integration:
.dockerignoresuggests Docker-based workflows are supported or facilitated. - GitHub CI configuration:
.githuband.travis.ymlfor 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.
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