ANN Benchmarks Parameter Tuning Guide#

This guide outlines the various parameter settings that can be specified in RAFT ANN Benchmark json configuration files and explains the impact they have on corresponding algorithms to help inform their settings for benchmarking across desired levels of recall.

RAFT Indexes#

raft_brute_force#

Use RAFT brute-force index for exact search. Brute-force has no further build or search parameters.

raft_ivf_flat#

IVF-flat uses an inverted-file index, which partitions the vectors into a series of clusters, or lists, storing them in an interleaved format which is optimized for fast distance computation. The searching of an IVF-flat index reduces the total vectors in the index to those within some user-specified nearest clusters called probes.

IVF-flat is a simple algorithm which won’t save any space, but it provides competitive search times even at higher levels of recall.

Parameter Type Required Data Type Default Description
nlist build Y Positive Integer >0 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
niter build N Positive Integer >0 20 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
ratio build N Positive Integer >0 2 1/ratio is the number of training points which should be used to train the clusters.
dataset_memory_type build N ["device", "host", "mmap"] "mmap" What memory type should the dataset reside?
query_memory_type search N ["device", "host", "mmap"] "device What memory type should the queries reside?
nprobe search Y Positive Integer >0 The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.

raft_ivf_pq#

IVF-pq is an inverted-file index, which partitions the vectors into a series of clusters, or lists, in a similar way to IVF-flat above. The difference is that IVF-PQ uses product quantization to also compress the vectors, giving the index a smaller memory footprint. Unfortunately, higher levels of compression can also shrink recall, which a refinement step can improve when the original vectors are still available.

Parameter Type Required Data Type Default Description
nlist build Y Positive Integer >0 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
niter build N Positive Integer >0 20 Number of k-means iterations to use when training the clusters.
ratio build N Positive Integer >0 2 1/ratio is the number of training points which should be used to train the clusters.
pq_dim build N Positive Integer. Multiple of 8. 0 Dimensionality of the vector after product quantization. When 0, a heuristic is used to select this value. pq_dim * pq_bits must be a multiple of 8.
pq_bits build N Positive Integer. [4-8] 8 Bit length of the vector element after quantization.
codebook_kind build N ["cluster", "subspace"] "subspace" Type of codebook. See the API docs for more detail
dataset_memory_type build N ["device", "host", "mmap"] "host" What memory type should the dataset reside?
query_memory_type search N ["device", "host", "mmap"] "device What memory type should the queries reside?
nprobe search Y Positive Integer >0 The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
internalDistanceDtype search N [float, half] half The precision to use for the distance computations. Lower precision can increase performance at the cost of accuracy.
smemLutDtype search N [float, half, fp8] half The precision to use for the lookup table in shared memory. Lower precision can increase performance at the cost of accuracy.
refine_ratio search N Positive Number >=1 1 refine_ratio * k nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best k neighbors.

raft_cagra#

CAGRA uses a graph-based index, which creates an intermediate, approximate kNN graph using IVF-PQ and then further refining and optimizing to create a final kNN graph. This kNN graph is used by CAGRA as an index for search.

Parameter Type Required Data Type Default Description
graph_degree build N Positive Integer >0 64 Degree of the final kNN graph index.
intermediate_graph_degree build N Positive Integer >0 128 Degree of the intermediate kNN graph.
graph_build_algo build N ["IVF_PQ", "NN_DESCENT"] "IVF_PQ" Algorithm to use for search
dataset_memory_type build N ["device", "host", "mmap"] "mmap" What memory type should the dataset reside while constructing the index?
query_memory_type search N ["device", "host", "mmap"] "device What memory type should the queries reside?
itopk search_wdith N Positive Integer >0 64 Number of intermediate search results retained during the search. Higher values improve search accuracy at the cost of speed.
search_width search N Positive Integer >0 1 Number of graph nodes to select as the starting point for the search in each iteration.
max_iterations search N Integer >=0 0 Upper limit of search iterations. Auto select when 0.
algo search N string "auto" Algorithm to use for search. Possible values: {"auto", "single_cta", "multi_cta", "multi_kernel"}
graph_memory_type search N string "device" Memory type to store gaph. Must be one of {"device", "host_pinned", "host_huge_page"}.
internal_dataset_memory_type search N string "device" Memory type to store dataset in the index. Must be one of {"device", "host_pinned", "host_huge_page"}.

The graph_memory_type or internal_dataset_memory_type options can be useful for large datasets that do not fit the device memory. Setting internal_dataset_memory_type other than device has negative impact on search speed. Using host_huge_page option is only supported on systems with Heterogeneous Memory Management or on platforms that natively support GPU access to system allocated memory, for example Grace Hopper.

To fine tune CAGRA index building we can customize IVF-PQ index builder options using the following settings. These take effect only if graph_build_algo == "IVF_PQ". It is recommended to experiment using a separate IVF-PQ index to find the config that gives the largest QPS for large batch. Recall does not need to be very high, since CAGRA further optimizes the kNN neighbor graph. Some of the default values are derived from the dataset size which is assumed to be [n_vecs, dim].

Parameter Type Required Data Type Default Description
ivf_pq_build_nlist build N Positive Integer >0 n_vecs / 2500 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
ivf_pq_build_niter build N Positive Integer >0 25 Number of k-means iterations to use when training the clusters.
ivf_pq_build_ratio build N Positive Integer >0 10 1/ratio is the number of training points which should be used to train the clusters.
ivf_pq_build_pq_dim build N Positive Integer. Multiple of 8. dim/2 rounded up to 8 Dimensionality of the vector after product quantization. When 0, a heuristic is used to select this value. pq_dim * pq_bits must be a multiple of 8.
ivf_pq_build_pq_bits build N Positive Integer. [4-8] 8 Bit length of the vector element after quantization.
ivf_pq_build_codebook_kind build N ["cluster", "subspace"] "subspace" Type of codebook. See the API docs for more detail
ivf_pq_search_nprobe build N Positive Integer >0 min(2*dim, nlist) The closest number of clusters to search for each query vector.
ivf_pq_search_internalDistanceDtype build N [float, half] fp8 The precision to use for the distance computations. Lower precision can increase performance at the cost of accuracy.
ivf_pq_search_smemLutDtype build N [float, half, fp8] half The precision to use for the lookup table in shared memory. Lower precision can increase performance at the cost of accuracy.
ivf_pq_search_refine_ratio build N Positive Number >=1 2 refine_ratio * k nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best k neighbors.

Alternatively, if graph_build_algo == "NN_DESCENT", then we can customize the following parameters

Parameter Type Required Data Type Default Description
nn_descent_niter build N Positive Integer>0 20 Number of NN Descent iterations.
nn_descent_intermediate_graph_degree build N Positive Integer>0 intermediate_graph_degree * 1.5 Intermadiate graph degree during NN descent iterations
nn_descent_max_iterations build N Positive Integer>0 20 Alias for nn_descent_niter
nn_descent_termination_threshold build N Positive float>0 0.0001 Termination threshold for NN descent.

raft_cagra_hnswlib#

This is a benchmark that enables interoperability between CAGRA built HNSW search. It uses the CAGRA built graph as the base layer of an hnswlib index to search queries only within the base layer (this is enabled with a simple patch to hnswlib).

build : Same as build of CAGRA

search : Same as search of hnswlib

FAISS Indexes#

faiss_gpu_flat#

Use FAISS flat index on the GPU, which performs an exact search using brute-force and doesn’t have any further build or search parameters.

faiss_gpu_ivf_flat#

IVF-flat uses an inverted-file index, which partitions the vectors into a series of clusters, or lists, storing them in an interleaved format which is optimized for fast distance computation. The searching of an IVF-flat index reduces the total vectors in the index to those within some user-specified nearest clusters called probes.

IVF-flat is a simple algorithm which won’t save any space, but it provides competitive search times even at higher levels of recall.

Parameter Type Required Data Type Default Description
nlists build Y Positive Integer >0 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
ratio build N Positive Integer >0 2 1/ratio is the number of training points which should be used to train the clusters.
nprobe search Y Positive Integer >0 The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.

faiss_gpu_ivf_pq#

IVF-pq is an inverted-file index, which partitions the vectors into a series of clusters, or lists, in a similar way to IVF-flat above. The difference is that IVF-PQ uses product quantization to also compress the vectors, giving the index a smaller memory footprint. Unfortunately, higher levels of compression can also shrink recall, which a refinement step can improve when the original vectors are still available.

Parameter Type Required Data Type Default Description
nlist build Y Positive Integer >0 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
ratio build N Positive Integer >0 2 1/ratio is the number of training points which should be used to train the clusters.
M_ratio build Y Positive Integer Power of 2 [8-64] Ratio of numbeer of chunks or subquantizers for each vector. Computed by dims / M_ratio
usePrecomputed build N Boolean. Default=false false Use pre-computed lookup tables to speed up search at the cost of increased memory usage.
useFloat16 build N Boolean. Default=false false Use half-precision floats for clustering step.
nprobe search Y Positive Integer >0 The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
refine_ratio search N Positive Number >=1 1 refine_ratio * k nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best k neighbors.

faiss_cpu_flat#

Use FAISS flat index on the CPU, which performs an exact search using brute-force and doesn’t have any further build or search parameters.

Parameter Type Required Data Type Default Description
numThreads search N Positive Integer >0 1 Number of threads to use for queries.

faiss_cpu_ivf_flat#

Use FAISS IVF-Flat index on CPU

Parameter Type Required Data Type Default Description
nlist build Y Positive Integer >0 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
ratio build N Positive Integer >0 2 1/ratio is the number of training points which should be used to train the clusters.
nprobe search Y Positive Integer >0 The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
numThreads search N Positive Integer >0 1 Number of threads to use for queries.

faiss_cpu_ivf_pq#

Use FAISS IVF-PQ index on CPU

Parameter Type Required Data Type Default Description
nlist build Y Positive Integer >0 Number of clusters to partition the vectors into. Larger values will put less points into each cluster but this will impact index build time as more clusters need to be trained.
ratio build N Positive Integer >0 2 1/ratio is the number of training points which should be used to train the clusters.
M build Y Positive Integer Power of 2 [8-64] Number of chunks or subquantizers for each vector.
usePrecomputed build N Boolean. Default=false false Use pre-computed lookup tables to speed up search at the cost of increased memory usage.
bitsPerCode build N Positive Integer [4-8] 8 Number of bits to use for each code.
nprobe search Y Positive Integer >0 The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
refine_ratio search N Positive Number >=1 1 refine_ratio * k nearest neighbors are queried from the index initially and an additional refinement step improves recall by selecting only the best k neighbors.
numThreads search N Positive Integer >0 1 Number of threads to use for queries.

HNSW#

hnswlib#

Parameter Type Required Data Type Default Description
efConstruction build Y Positive Integer >0 Controls index time and accuracy. Bigger values increase the index quality. At some point, increasing this will no longer improve the quality.
M build Y Positive Integer often between 2-100 Number of bi-directional links create for every new element during construction. Higher values work for higher intrinsic dimensionality and/or high recall, low values can work for datasets with low intrinsic dimensionality and/or low recalls. Also affects the algorithm's memory consumption.
numThreads build N Positive Integer >0 1 Number of threads to use to build the index.
ef search Y Positive Integer >0 Size of the dynamic list for the nearest neighbors used for search. Higher value leads to more accurate but slower search. Cannot be lower than k.
numThreads search N Positive Integer >0 1 Number of threads to use for queries.

Please refer to HNSW algorithm parameters guide from hnswlib to learn more about these arguments.

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