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

cuVS Indexes

cuvs_brute_force

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

cuvs_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.

cuvs_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.

cuvs_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.

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	sqrt(n_vecs)	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_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 IVF-PQ index overview for more detail
ivf_pq_build_nprobe	search	N	Positive integer >0	min(2*dim, nlist)	The closest number of clusters to search for each query vector. Larger values will improve recall but will search more points in the index.
ivf_pq_build_internalDistanceDtype	search	N	[float, half]	half	The precision to use for the distance computations. Lower precision can increase performance at the cost of accuracy.
ivf_pq_build_smemLutDtype	search	N	[float, half, fp8]	fp8	The precision to use for the lookup table in shared memory. Lower precision can increase performance at the cost of accuracy.
ivf_pq_build_refine_ratio	search	N	Positive integer >0	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	cagra.intermediate_graph_degree * 1.5	Intermadiate graph degree during nn-descent iterations
nn_descent_termination_threshold	build	N	Positive float >0	1e-4	Early stopping threshold for nn-descent convergence

cuvs_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	false	Use pre-computed lookup tables to speed up search at the cost of increased memory usage.
useFloat16	build	N	Boolean	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
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.
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]		Ratio of number of chunks or subquantizers for each vector. Computed by dims / M_ratio
usePrecomputed	build	N	Boolean	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 for representing each quantized 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		umber 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.