Dependencies

CUDA 11 and a GPU with Pascal architecture or later are required to run the benchmarks.

Please refer to the installation docs for the base requirements to build RAFT.

In addition to the base requirements for building RAFT, additional dependencies needed to build the ANN benchmarks include:

1. FAISS GPU >= 1.7.1

2. Google Logging (GLog)

3. H5Py

4. HNSWLib

5. nlohmann_json

6. GGNN

rapids-cmake is used to build the ANN benchmarks so the code for dependencies not already supplied in the CUDA toolkit will be downloaded and built automatically.

The easiest (and most reproducible) way to install the dependencies needed to build the ANN benchmarks is to use the conda environment file located in the conda/environments directory of the RAFT repository. The following command will use mamba (which is preferred over conda) to build and activate a new environment for compiling the benchmarks:

mamba env create --name raft_ann_benchmarks -f conda/environments/bench_ann_cuda-118_arch-x86_64.yaml
conda activate raft_ann_benchmarks

The above conda environment will also reduce the compile times as dependencies like FAISS will already be installed and not need to be compiled with rapids-cmake.

Compiling the Benchmarks

After the needed dependencies are satisfied, the easiest way to compile ANN benchmarks is through the build.sh script in the root of the RAFT source code repository. The following will build the executables for all the support algorithms:

./build.sh bench-ann

You can limit the algorithms that are built by providing a semicolon-delimited list of executable names (each algorithm is suffixed with _ANN_BENCH):

./build.sh bench-ann -n --limit-bench-ann=HNSWLIB_ANN_BENCH;RAFT_IVF_PQ_ANN_BENCH

Available targets to use with --limit-bench-ann are:

• FAISS_IVF_FLAT_ANN_BENCH

• FAISS_IVF_PQ_ANN_BENCH

• FAISS_BFKNN_ANN_BENCH

• GGNN_ANN_BENCH

• HNSWLIB_ANN_BENCH

• RAFT_CAGRA_ANN_BENCH

• RAFT_IVF_PQ_ANN_BENCH

• RAFT_IVF_FLAT_ANN_BENCH

By default, the *_ANN_BENCH executables program infer the dataset’s datatype from the filename’s extension. For example, an extension of fbin uses a float datatype, f16bin uses a float16 datatype, extension of i8bin uses int8_t datatype, and u8bin uses uint8_t type. Currently, only float, float16, int8_t, and unit8_t` are supported.