Dedicated hardware-aware optimization for RaBitQ

Determine whether dedicated hardware-aware optimizations tailored specifically to RaBitQ can further improve its performance beyond the current approach of inheriting Product Quantization (PQ) SIMD-based optimizations by reducing RaBitQ’s computations to those of PQ. This entails designing and evaluating specialized low-level implementations (e.g., SIMD/GPU/FPGA) that exploit RaBitQ’s simpler bitwise-operation nature to potentially surpass existing PQ-focused hardware optimizations.

Background

The paper notes that many practical gains for Product Quantization (PQ) arise from hardware-aware optimizations leveraging SIMD instructions. To benefit from this, RaBitQ reduces its computation to PQ’s form where possible, thereby inheriting existing SIMD-based optimizations.

However, RaBitQ naturally supports much simpler bitwise operations than PQ, suggesting that specialized hardware-aware implementations tuned directly for RaBitQ’s computation pattern might yield further improvements. The authors explicitly identify as an open question whether such dedicated optimizations can enhance RaBitQ’s performance beyond what is achieved by reusing PQ’s hardware optimizations.

References

However, RaBitQ, in its nature, can be implemented with much simpler bitwise operations (which is not possible for PQ and its variants). It remains to be an interesting question whether dedicated hardware-aware optimization can further improve the performance of RaBitQ.

— RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search (2405.12497 - Gao et al., 21 May 2024) in Section 6 (Related Work)