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Co-authored-by: Keith Kraus <[email protected]>

Author: leofang

README.md

@@ -4,9 +4,9 @@ CUDA Python is the home for accessing NVIDIA’s CUDA platform from Python. It c
 
 * [cuda.core](https://nvidia.github.io/cuda-python/cuda-core/latest): Pythonic access to CUDA Runtime and other core functionalities
 * [cuda.bindings](https://nvidia.github.io/cuda-python/cuda-bindings/latest): Low-level Python bindings to CUDA C APIs
-* [cuda.cooperative](https://nvidia.github.io/cccl/cuda_cooperative/): A Python package providing CUB's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
-* [cuda.parallel](https://nvidia.github.io/cccl/cuda_parallel/): A Python package for easy access to highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc, that are callable on the *host*.
-* [numba.cuda](https://nvidia.github.io/numba-cuda/): Numba's CUDA target for writing CUDA SIMT kernels in Python.
+* [cuda.cooperative](https://nvidia.github.io/cccl/cuda_cooperative/): A Python package providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
+* [cuda.parallel](https://nvidia.github.io/cccl/cuda_parallel/): A Python package for easy access to CCCL's highly efficient and customizable parallel algorithms, like `sort`, `scan`, `reduce`, `transform`, etc, that are callable on the *host*
+* [numba.cuda](https://nvidia.github.io/numba-cuda/): Numba's target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
 
 For access to NVIDIA CPU & GPU Math Libraries, please refer to [nvmath-python](https://docs.nvidia.com/cuda/nvmath-python/latest).
 

cuda_bindings/docs/source/install.md

@@ -46,11 +46,11 @@ $ conda install -c conda-forge cuda-python
 ### Requirements
 
 * CUDA Toolkit headers[^1]
-* static CUDA runtime[^2]
+* CUDA Runtime static library[^2]
 
 [^1]: User projects that `cimport` CUDA symbols in Cython must also use CUDA Toolkit (CTK) types as provided by the `cuda.bindings` major.minor version. This results in CTK headers becoming a transitive dependency of downstream projects through CUDA Python.
 
-[^2]: The static CUDA runtime (`libcudart_static.a` on Linux, `cudart_static.lib` on Windows) is part of CUDA Toolkit. If CUDA is installed from conda, it is contained in the `cuda-cudart-static` package.
+[^2]: The CUDA Runtime static library (`libcudart_static.a` on Linux, `cudart_static.lib` on Windows) is part of the CUDA Toolkit. If using conda packages, it is contained in the `cuda-cudart-static` package.
 
 Source builds require that the provided CUDA headers are of the same major.minor version as the `cuda.bindings` you're trying to build. Despite this requirement, note that the minor version compatibility is still maintained. Use the `CUDA_HOME` (or `CUDA_PATH`) environment variable to specify the location of your headers. For example, if your headers are located in `/usr/local/cuda/include`, then you should set `CUDA_HOME` with:
 

cuda_python/docs/source/index.rst

@@ -6,9 +6,9 @@ multiple components:
 
 - `cuda.core`_: Pythonic access to CUDA runtime and other core functionalities
 - `cuda.bindings`_: Low-level Python bindings to CUDA C APIs
-- `cuda.cooperative`_: A Python package providing CUB's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
-- `cuda.parallel`_: A Python package for easy access to highly efficient and customizable parallel algorithms, like ``sort``, ``scan``, ``reduce``, ``transform``, etc, that are callable on the *host*
-- `numba.cuda`_: Numba's CUDA target for writing CUDA SIMT kernels in Python
+- `cuda.cooperative`_: A Python package providing CCCL's reusable block-wide and warp-wide *device* primitives for use within Numba CUDA kernels
+- `cuda.parallel`_: A Python package for easy access to CCCL's highly efficient and customizable parallel algorithms, like ``sort``, ``scan``, ``reduce``, ``transform``, etc, that are callable on the *host*
+- `numba.cuda`_: Numba's target for CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model.
 
 For access to NVIDIA CPU & GPU Math Libraries, please refer to `nvmath-python`_.