TSDG

Introduction

TSDG is an index graph for graph-based approximate nearest neighbor search, which is fast to build, efficient to search, and GPU friendly.

Building

$ cd tsdg$ mkdir build && cd build$ cmake ..$ make -j

Usage

The demos for building index and searching are under the directory examples/

Building Index

Step 1. Build Shortcut (SC) Graph

Firstly, we need to prepare a SC-graph.The SC-graph need to be built in advance, we constructed SC-graph by modified NN-Descent in nndescent.hpp

You can use our example code to build the SC-graph as follows:

$ build/examples/scg_build DATA_PATH K SAMPLE_NUM ITERATION_NUM ADD_SHORTCUTS OUTPUT_PATH
  • DATA_PATH is the path of the base data in fvecs format.
  • K is the number of edges of the SC-Graph.
  • SAMPLE_NUM controls the number of samples in each NN-Descent iteration.
  • ITERATION_NUM controls the number of NN-Descent iterations.
  • OUTPUT_PATH is the path of the generated SC-graph.

An example:

$ build/examples/scg_build \    ./data/sift1m/sift1m_base.fvecs \    200 16 12\    ./data/sift1m/index/sift1m_scg.ivecs

The following are the parameters used for building the augmented k-NN graphs on the CPU in our paper.

Dataset k of k-NN graph SAMPLE_NUM ITERATION_NUM
SIFT1M 200 16 10
DEEP1M 200 16 10
T2I1M 400 16 10
SPACEV1M 200 20 12
GIST1M 400 16 12
Turing1M 200 20 15

Note: The above parameter settings consider the balance of building efficiency and search efficiency, appropriately increasing SAMPLE_NUM and ITERATION_NUM may improve the search efficiency.

The above datasets can be downloaded from http://corpus-texmex.irisa.fr/ and https://big-ann-benchmarks.com/

Step 2. Convert the augmented k-NN graph to TSDG index

Secondly, we will convert the augmented k-NN graph to the TSDG index.

You can use our example code to achieve this conversion as follows:

$ build/examples/tsdg_build DATA_PATH SCG_PATH BUILD_MODE LE_THRESHOLD RANK_THRESHOLD MAX_EDGES OUTPUT_PATH
  • DATA_PATH is the path of the base data in fvecs format.
  • SCG_PATH is the path of the pre-built SC-graph in ivecs format.
  • RELAXED_FACTOR used for first-stage graph diversification, controls the average degree of index graph.
  • OCCL_THRESHOLD used for second-stage graph diversification, controls the average degree of index graph.
  • MAX_EDGES used for limiting the degree of index graph.
  • OUTPUT_PATH is the path of the generated TSDG index.

An example:

$ build/examples/tsdg_build \    ./data/sift1m/sift1m_base.fvecs \    ./data/sift1m/sift1m_scg.ivecs \    1.2 4 60 \    ./data/sift1m/index/sift1m.tsdg

The parameters RELAXED_FACTOR in the source code corresponds to the parameter $\alpha$ for Relaxed Graph Diversification in the paper.

The parameters OCCL_THRESHOLD in the source code corresponds to the parameter $\lambda_0$ for Soft Graph Diversification in the paper.

The following are the parameters used for building and searching experiments on the CPU.

Dataset RELAXED_FACTOR OCCL_THRESHOLD MAX_EDGES
SIFT1M 1.2 4 60
DEEP1M 1.2 4 60
T2I1M 1.2 4 60
SPACEV1M 1.2 4 80
GIST1M 1.1 8 80
Turing1M 1.2 4 80

Note: The above parameter settings consider the efficiency of graph construction and the consistency of parameters. The search performance is better when the parameter RELAXED_FACTOR of T2I1M and SPACEV1M is 1.3, but it will increase the time of graph construction.

The index graph building time (s) is shown below (including the k-NN graph building time):

Dataset TSDGâș NSG SSG DPG HNSW
SIFT1M 307.5 492.8 359.8 462.8 364.0
DEEP1M 282.1 449.5 320.4 400.9 326.1
T2I1M 595.1 1472.4 913.9 1888.9 937.2
SPACEV1M 539.9 657.9 676.8 659.5 615.4
GIST1M 1363.7 2794.6 1728.0 6138.7 1595.9
Turing1M 726.5 1267.8 1052.9 827.6 792.7

Search on the CPU

You can use our demo code to search on graph as follows:

build/examples/tsdg_search INDEX_PATH QUERY_PATH GROUND_TRUTH_PATH

An example:

$ build/examples/tsdg_search \    ./data/sift1m/index/sift1m.tsdg \    ./data/sift1m/sift1m_query.fvecs \    ./data/sift1m/sift1m_gt.ivecs

The performance of the search on the graph is shown below:

Search result

Search on the GPU

Due to licensing issues, this library is not able to provide the code on the GPU, the GPU related code will be open source in the future.

GitHub

View Github