# Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD

arXiv: https://arxiv.org/abs/1812.09162 · HTML: https://mlsanity.com/paper/1812.09162/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 14 of 14)

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- [Multiplying Matrices Without Multiplying](https://mlsanity.com/paper/2106.10860/) (2021)
- [Similarity search in the blink of an eye with compressed indices](https://mlsanity.com/paper/2304.04759/) (2023)
- [Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2206.01382/) (2022)
- [Binary Embedding-based Retrieval at Tencent](https://mlsanity.com/paper/2302.08714/) (2023)
- [Nearest neighbor search with compact codes: A decoder perspective](https://mlsanity.com/paper/2112.09568/) (2021)
- [LeanVec: Searching vectors faster by making them fit](https://mlsanity.com/paper/2312.16335/) (2023)
- [Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search](https://mlsanity.com/paper/2405.12207/) (2024)
- [ARM 4-BIT PQ: SIMD-based Acceleration for Approximate Nearest Neighbor Search on ARM](https://mlsanity.com/paper/2203.02505/) (2022)
- [LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table](https://mlsanity.com/paper/2506.04790/) (2025)
- [Vector search with small radiuses](https://mlsanity.com/paper/2403.10746/) (2024)
- [GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction](https://mlsanity.com/paper/2410.22347/) (2024)
- [Individualized non-uniform quantization for vector search](https://mlsanity.com/paper/2509.18471/) (2025)
- [Passing the Baton: High Throughput Distributed Disk-Based Vector Search with BatANN](https://mlsanity.com/paper/2512.09331/) (2025)

## Abstract (arXiv)

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. A common approach is to rely on Product Quantization, which allows the storage of large vector databases in memory and efficient distance computations. Yet, implementations of nearest neighbor search with Product Quantization have their performance limited by the many memory accesses they perform. Following this observation, Andr'e et al. proposed Quick ADC with up to 6× faster implementations of m×4 product quantizers (PQ) leveraging specific SIMD instructions. Quicker ADC is a generalization of Quick ADC not limited to m×4 codes and supporting AVX-512, the latest revision of SIMD instruction set. In doing so, Quicker ADC faces the challenge of using efficiently 5,6 and 7-bit shuffles that do not align to computer bytes or words. To this end, we introduce (i) irregular product quantizers combining sub-quantizers of different granularity and (ii) split tables allowing lookup tables larger than registers. We evaluate Quicker ADC with multiple indexes including Inverted Multi-Indexes and IVF HNSW and show that it outperforms the reference optimized implementations (i.e., FAISS and polysemous codes) for numerous configurations. Finally, we release an open-source fork of FAISS enhanced with Quicker ADC at http://github.com/nlescoua/faiss-quickeradc.