final adjustments to the docs

docs/source/installation.md

@@ -41,7 +41,7 @@ This requires a working installation of [Cupy](https://cupy.dev).
 Please refer to the cupy's [installation manual](https://docs.cupy.dev/en/stable/install.html)
 for details about installing cupy.
 
-You can also use Cupy together with AMD ROCm for AMD graphics cards,
+You can also use Cupy together with AMD ROCm and HIP for AMD graphics cards,
 but the setup steps are a bit more complicated - you might have to build cupy from source.
 The Cupy documentation covers this in their [installation guide for Cupy on ROCm][cupy-rocm].
 

docs/source/user_manual/gpu_kernels.md

@@ -39,7 +39,7 @@ which operates much in the same way that [NumPy][numpy] works on CPU arrays.
 Cupy and NumPy expose nearly the same APIs for array operations;
 the difference being that CuPy allocates all its arrays on the GPU
 and performs its operations as CUDA kernels.
-Also, CuPy exposes a just-in-time-compiler for GPU kernels, which internally calls [nvrtc].
+Also, CuPy exposes a just-in-time-compiler for GPU kernels.
 In pystencils, we use CuPy both to compile and provide executable kernels on-demand from within Python code,
 and to allocate and manage the data these kernels can be executed on.
 
@@ -271,5 +271,4 @@ only a part of the triangle is being processed.
 
 [cupy]: https://cupy.dev "CuPy Homepage"
 [numpy]: https://numpy.org "NumPy Homepage"
-[nvrtc]: https://docs.nvidia.com/cuda/nvrtc/index.html "NVIDIA Runtime Compilation Library"
 [cupy-docs]: https://docs.cupy.dev/en/stable/overview.html "CuPy Documentation"