Nanvivi/SIFT100M-DiskANN

SIFT-100M Dataset & Disk Index

The SIFT-100M dataset (first 100M vectors of BigANN SIFT-1B) and pre-built disk-based ANN index. Built February 2026 on Intel Xeon 8462Y+ (Sapphire Rapids).

Build Parameters

Parameter	Value
Dataset	SIFT-100M (100,000,000 vectors, 128-dim, uint8)
Graph R	96 (max degree)
Build L	128 (search list size during construction)
PQ chunks	32 (4 dimensions per sub-quantizer)

Files

Raw Data

File	Size	Description
base.bin	12GB	Raw SIFT-100M vectors. Binary format: [uint32 num_points][uint32 dim][uint8 vectors...]. 100M x 128-dim x uint8.
query.bin	1.3MB	10,000 query vectors in the same format. Standard BigANN query set.
gt.bin	39MB	Ground truth (100 nearest neighbors per query). Binary format: [uint32 num_queries][uint32 K][uint32 ids...].

Disk Index

File	Size	Description
disk.diskidx	55GB	Sector-aligned Vamana graph on disk. Each 4096-byte sector contains one or more nodes. Each node stores: [float32 coords[128]][uint32 num_neighbors][uint32 neighbor_ids[R]]. Primary structure for graph-based disk ANN search.
disk.pq	129KB	PQ codebook (trained centroids). 32 sub-quantizers, each with 256 centroids of 4 float32 values. Used to compute approximate distances without reading full vectors from disk.
disk.pqcodes	3.0GB	PQ-compressed representation of all 100M vectors. Each vector encoded as 32 bytes (one centroid ID per sub-quantizer). Loaded into memory for fast approximate distance computation during search.

In-Memory Sample Index

File	Size	Description
mem	232MB	In-memory Vamana graph built on a 1% sample (~1M points). Provides fast entry point discovery — search this small graph first, then jump into the full disk graph at promising locations.
mem.data	123MB	Raw vectors for the 1% sample points. Kept in memory for exact distance computation during sample graph traversal.
mem.tags	3.9MB	Maps sample graph node IDs to their corresponding IDs in the full 100M dataset, so sample graph results can be used as entry points into the disk graph.