DiskANN: A Disk-based ANNS Solution with High Recall and High QPS on Billion-scale Dataset

"DiskANN: A Disk-based ANNS Solution with High Recall and High QPS on Billion-scale Dataset" is a paper published in NeurIPS 2019 that introduces an efficient method for index building and search on billion-scale datasets using a single machine. The proposed scheme, called DiskANN, builds a graph-based index on the dataset SIFT-1B with a single machine having 64GB of RAM and a 16-core CPU, achieving over 95% recall@1 at more than 5000 queries per second (QPS) with an average latency lower than 3ms. The paper also introduces Vamana, a new graph-based algorithm that minimizes the number of disk accesses and enhances search performance. DiskANN effectively supports search on large-scale datasets by overcoming memory restrictions in a single machine.