High-performance out-of-core vector index winner of NeurIPS'21 billion-scale ANN competition, leveraging disk-based structures for massive datasets beyond RAM limits. Employs advanced approximate search algorithms for high QPS on limited hardware. Applicable to large-scale recommendations and search; competitive with DiskANN baseline, outperforms in benchmarks unlike pure in-memory like Qdrant.
BBANN was the Track 2 winner in the NeurIPS'21 Billion-Scale Approximate Nearest Neighbor Search Competition. Track 2 focuses on out-of-core indices where, in addition to limited DRAM, the index can use an SSD for search. The hardware uses Azure Standard_L8s_v2 VMs with 8 vCPUs, 64GB RAM, and a local SSD constrained to 1TB.
| Property | Value |
|---|---|
| Track | T2 (Out-of-core indices) |
| Baseline | DiskANN |
| Target QPS | 1,500 queries/sec |
| Hardware | Azure Standard_L8s_v2 (8 vCPUs + 64GB RAM + 1TB SSD) |
Xiaomeng Yi, Xiaofan Luan, Weizhi Xu, Qianya Cheng, Jigao Luo, Xiangyu Wang, Jiquan Long, Xiao Yan, Zheng Bian, Jiarui Luo, Shengjun Li, Chengming Li from Zilliz and Southern University of Science and Technology.
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