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



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

Quicker ADC : Unlocking the hidden potential of Product Quantization with SIMD
Research Paper / IEEE Transactions on Pattern Analysis and Machine Intelligence / Nov 2019 / Network & Communications, Machine/Deep Learning/AI

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, Andre et al. proposed Quick ADC with up to 6x faster implementations of mx4 product quantizers (PQ) leveraging specific SIMD instructions. Quicker ADC is a generalization of Quick ADC not limited to mx4 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.