This paper presents an interpretable analysis framework for PDET-LSH, focusing on parameter sensitivity, quality-efficiency tradeoffs, and parallel scaling characteristics. Comprehensive experiments on synthetic datasets reveal the effects of LSH parameters on recall, query time, and memory usage, providing practical guidelines for parameter selection.
Key findings
PDET-LSH achieves up to 95% recall with sub-linear query complexity.
Near-linear speedup up to 16 cores for both indexing and querying is demonstrated.
Proper parameter tuning can significantly enhance performance and quality guarantees.
Limitations & open questions
The study focuses on synthetic datasets; real-world data performance may vary.
The analysis framework's applicability to other ANN search methods is not explored.