Review for NeurIPS paper: HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory

The paper makes an inaccurate claim about the presence of billion-scale ANNS solutions. The performance gain of the proposed HM-ANN algorithm seems marginal when considering its learning curve in practice. The experiments do not evaluate the performance of data fetching. So it is hard to conclude that the proposed HM-ANN achieves better utilization of HM. The paper claims that the proposed HM-ANN is the first billion-scale ANNS solution on a single machine, without using compression (see the last paragraph of Introduction).