Milvus KBest Algorithm Optimization
Application scenario: High performance with high precision is required.
Technical principle: Before search, KBest obtains the optimal prefetch parameters of the current graph structure through prefetch parameter optimization. During search, KBest quickly approaches the query point with the help of the entry point pre-stored in the graph index, identifies the query point's near neighbors, and accelerates distance calculation using the SIMD instructions. When the search is complete, KBest performs full-precision or half-precision rearrangement to improve the ranking precision and returns the k-nearest neighbors.
Performance metric: Compare with the Milvus-HNSW algorithm, the Milvus-KBest algorithm improves QPS performance by 30% on the ANN-Benchmarks GIST dataset with a recall value greater than 0.99 and the configuration of 16 vCPUs and 64 GB memory.
