Paper Reading｜HM-ANN: When ANNS Meets Heterogeneous Memory

The research paper "HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogenous Memory" proposes a novel algorithm called HM-ANN for graph-based similarity search. This algorithm considers both memory heterogeneity and data heterogeneity in modern hardware settings, enabling billion-scale similarity search on a single machine without compression technologies. The paper discusses the challenges of existing approximate nearest neighbor (ANN) search solutions due to limited dynamic random-access memory (DRAM) capacity and presents HM-ANN as an efficient alternative that achieves low search latency and high search accuracy, especially when the dataset cannot fit into DRAM.

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