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Despite the broad range of algorithms for Approximate Nearest NeighborSearch, most empirical evaluations of algorithms have focused on smallerdatasets, typically of 1 million points~\citep{Benchmark}. However, deployingrecent advances in embedding based techniques for search, recommendation andranking at scale require ANNS indices at billion, trillion or larger scale.Barring a few recent papers, there is limited consensus on which algorithms areeffective at this scale vis-\`a-vis their hardware cost.  This competition compares ANNS algorithms at billion-scale by hardware cost,accuracy and performance. We set up an open source evaluation framework andleaderboards for both standardized and specialized hardware. The competitioninvolves three tracks. The standard hardware track T1 evaluates algorithms onan Azure VM with limited DRAM, often the bottleneck in serving billion-scaleindices, where the embedding data can be hundreds of GigaBytes in size. It usesFAISS~\citep{Faiss17} as the baseline. The standard hardware track T2additional allows inexpensive SSDs in addition to the limited DRAM and usesDiskANN~\citep{DiskANN19} as the baseline. The specialized hardware track T3allows any hardware configuration, and again uses FAISS as the baseline.  We compiled six diverse billion-scale datasets, four newly released for thiscompetition, that span a variety of modalities, data types, dimensions, deeplearning models, distance functions and sources. The outcome of the competitionwas ranked leaderboards of algorithms in each track based on recall at a querythroughput threshold. Additionally, for track T3, separate leaderboards werecreated based on recall as well as cost-normalized and power-normalized querythroughput.

Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search