[skip-ci] Updating minimum required gcc version

README.md

@@ -20,10 +20,11 @@ While not exhaustive, the following general categories help summarize the accele
 | **Statistics** | sampling, moments and summary statistics, metrics |
 | **Tools & Utilities** | common utilities for developing CUDA applications, multi-node multi-gpu infrastructure |
 
-RAFT provides a header-only C++ library and pre-compiled shared libraries that can 1) speed up compile times and 2) enable the APIs to be used without CUDA-enabled compilers.
+
+All of RAFT's C++ APIs can be accessed header-only and optional pre-compiled shared libraries can 1) speed up compile times and 2) enable the APIs to be used without CUDA-enabled compilers.
 
 In addition to the C++ library, RAFT also provides 2 Python libraries:
-- `pylibraft` - lightweight low-level Python wrappers around RAFT algorithms and primitives.
+- `pylibraft` - lightweight low-level Python wrappers around RAFT's host-accessable APIs.
 - `raft-dask` - multi-node multi-GPU communicator infrastructure for building distributed algorithms on the GPU with Dask.
 
 ## Getting started
@@ -78,7 +79,7 @@ raft::distance::pairwise_distance(handle, input.view(), input.view(), output.vie
 
 ### Python Example
 
-The `pylibraft` package contains a Python API for RAFT algorithms and primitives. `pylibraft` integrates nicely into other libraries by being very lightweight with minimal dependencies and accepting any object that supports the `__cuda_array_interface__`, such as [CuPy's ndarray](https://docs.cupy.dev/en/stable/user_guide/interoperability.html#rmm). The package is currently limited to pairwise distances and RMAT graph generation, but we will continue adding more in future releases.
+The `pylibraft` package contains a Python API for RAFT algorithms and primitives. `pylibraft` integrates nicely into other libraries by being very lightweight with minimal dependencies and accepting any object that supports the `__cuda_array_interface__`, such as [CuPy's ndarray](https://docs.cupy.dev/en/stable/user_guide/interoperability.html#rmm). The number of RAFT algorithms exposed in this package is continuing to grow from release to release.
 
 The example below demonstrates computing the pairwise Euclidean distances between CuPy arrays. `pylibraft` is a low-level API that prioritizes efficiency and simplicity over being pythonic, which is shown here by pre-allocating the output memory before invoking the `pairwise_distance` function. Note that CuPy is not a required dependency for `pylibraft`.
 
@@ -127,7 +128,7 @@ After [installing](https://github.com/rapidsai/rapids-cmake#installation) rapids
 
 ```cmake
 
-set(RAFT_VERSION "22.04")
+set(RAFT_VERSION "22.12")
 set(RAFT_FORK "rapidsai")
 set(RAFT_PINNED_TAG "branch-${RAFT_VERSION}")
 

docs/source/build.md

@@ -3,6 +3,7 @@
 ## Building and installing RAFT
 
 ### CUDA/GPU Requirements
+- GCC 9.0+ (9.5.0+ recommended)
 - CUDA Toolkit 11.0+
 - NVIDIA driver 450.80.02+
 - Pascal architecture of better (compute capability >= 6.0)
@@ -18,14 +19,14 @@ In addition to the libraries included with cudatoolkit 11.0+, there are some oth
 #### Optional
 - [cuCollections](https://github.com/NVIDIA/cuCollections) - Used in `raft::sparse::distance` API.
 - [Libcu++](https://github.com/NVIDIA/libcudacxx) v1.7.0
-- [FAISS](https://github.com/facebookresearch/faiss) v1.7.0 - Used in `raft::spatial::knn` API and needed to build tests.
-- [NCCL](https://github.com/NVIDIA/nccl) - Used in `raft::comms` API and needed to build `raft-dask`
-- [UCX](https://github.com/openucx/ucx) - Used in `raft::comms` API and needed to build `raft-dask`
+- [FAISS](https://github.com/facebookresearch/faiss) v1.7.0 - Used in `raft::neighbors` API..
+- [NCCL](https://github.com/NVIDIA/nccl) - Used in `raft::comms` API and needed to build `raft-dask`.
+- [UCX](https://github.com/openucx/ucx) - Used in `raft::comms` API and needed to build `raft-dask`.
 - [Googletest](https://github.com/google/googletest) - Needed to build tests
 - [Googlebench](https://github.com/google/benchmark) - Needed to build benchmarks
 - [Doxygen](https://github.com/doxygen/doxygen) - Needed to build docs
 
-C++ RAFT is a header-only library but provides the option of building shared libraries with template instantiations for common types to speed up compile times for larger projects.
+All of RAFT's C++ APIs can be used header-only but pre-compiled shared libraries also contain some host-accessable APIs and template instantiations to accelerate compile times.
 
 The recommended way to build and install RAFT is to use the `build.sh` script in the root of the repository. This script can build both the C++ and Python artifacts and provides options for building and installing the headers, tests, benchmarks, and individual shared libraries.
 
@@ -189,7 +190,7 @@ There are two different strategies for including RAFT in downstream projects, de
 
 ### C++ header-only integration using cmake
 
-When the needed [build dependencies](#required_depenencies) are already satisfied, RAFT can be trivially integrated into downstream projects by cloning the repository and adding `cpp/include` from RAFT to the include path:
+When the needed [build dependencies](#build-dependencies) are already satisfied, RAFT can be trivially integrated into downstream projects by cloning the repository and adding `cpp/include` from RAFT to the include path:
 ```cmake
 set(RAFT_GIT_DIR ${CMAKE_CURRENT_BINARY_DIR}/raft CACHE STRING "Path to RAFT repo")
 ExternalProject_Add(raft
@@ -226,7 +227,7 @@ The following `cmake` snippet enables a flexible configuration of RAFT:
 
 ```cmake
 
-set(RAFT_VERSION "22.10")
+set(RAFT_VERSION "22.12")
 set(RAFT_FORK "rapidsai")
 set(RAFT_PINNED_TAG "branch-${RAFT_VERSION}")