MOre docs updates

docs/source/build.rst

@@ -45,14 +45,14 @@ C/C++ Package
 
 .. code-block:: bash
 
-    mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
+   mamba install -c rapidsai -c conda-forge -c nvidia libcuvs cuda-version=12.5
 
 Python Package
 ~~~~~~~~~~~~~~
 
 .. code-block:: bash
 
-    mamba install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.5
+   mamba install -c rapidsai -c conda-forge -c nvidia cuvs cuda-version=12.5
 
 Python through Pip
 ^^^^^^^^^^^^^^^^^^

docs/source/cuvs_bench/build.rst

@@ -0,0 +1,59 @@
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Build cuVS Bench From Source
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Dependencies
+============
+
+CUDA 11 and a GPU with Pascal architecture or later are required to run the benchmarks.
+
+Please refer to the  :doc:`installation docs <../build>` for the base requirements to build cuVS.
+
+In addition to the base requirements for building cuVS, 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 <https://github.com/rapidsai/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 cuVS repository. The following command will use `mamba` (which is preferred over `conda`) to build and activate a new environment for compiling the benchmarks:
+
+.. code-block:: bash
+
+    mamba env create --name cuvs_benchmarks -f conda/environments/cuvs_bench_cuda-118_arch-x86_64.yaml
+    conda activate cuvs_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:
+
+.. code-block:: bash
+
+    ./build.sh cuvs-bench
+
+You can limit the algorithms that are built by providing a semicolon-delimited list of executable names (each algorithm is suffixed with `_ANN_BENCH`):
+
+.. code-block:: bash
+
+    ./build.sh cuvs-bench -n --limit-bench-ann=HNSWLIB_ANN_BENCH;CUVS_IVF_PQ_ANN_BENCH
+
+Available targets to use with `--limit-bench-ann` are:
+- FAISS_GPU_IVF_FLAT_ANN_BENCH
+- FAISS_GPU_IVF_PQ_ANN_BENCH
+- FAISS_CPU_IVF_FLAT_ANN_BENCH
+- FAISS_CPU_IVF_PQ_ANN_BENCH
+- FAISS_GPU_FLAT_ANN_BENCH
+- FAISS_CPU_FLAT_ANN_BENCH
+- GGNN_ANN_BENCH
+- HNSWLIB_ANN_BENCH
+- CUVS_CAGRA_ANN_BENCH
+- CUVS_IVF_PQ_ANN_BENCH
+- CUVS_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.