PANTHER: Private Approximate Nearest Neighbor Search in the Single Server Setting

Explores and benchmarks distance comparison operators for ANN. arXiv preprint arXiv:2403.13491 (2024) by Zeyu Wang et al. Aids in vector search optimization.

Distributed disk-based approximate nearest neighbor system achieving near-linear throughput scaling. Delivers 6.21-6.49x throughput improvement over scatter-gather baseline with sub-6ms latency on 10 servers.

Manifold-Consistent Graph Indexing for billion-scale disk-resident vector search. Leverages Local Intrinsic Dimensionality to achieve 5.8x throughput improvement over DiskANN on high-dimensional datasets.

Incremental in-place update system for billion-scale vector search from Microsoft Research. Maintains 2.41x lower P99.9 latency than baselines while supporting efficient vector updates with minimal resource overhead.

Graph-based similarity search algorithm achieving 0.99 recall, building indexes 3-8x faster than competitors with 40% less memory. Answers 1-NN queries up to 10x faster than serial scan.

BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.