Introduce HIP as a dedicated code generation platform.
pystencils.codegen
Add Target.HIP
Change Target.GPU to alias Target.CurrentGPU
Auto-detect Target.CUDA or Target.HIP on GPU systems when using Target.CurrentGPU
pystencils.backend
Move all common functionality of CUDA and HIP to GenericGpu platform class
Introduce HipPlatform to inherit from GenericGpu
pystencils.jit
Adapt the CupyJit to only compile CUDA kernels on NVidia platforms, and only HIP kernels on ROCm platforms
While HIP can technically act as a shallow wrapper around CUDA on NVidia systems, this does not make sense here,
since a) we're gaining portability through code generation anyway, b) hipcc will just call nvcc on Nvidia systems anyway,
and c) cupy needs to be built against the entire ROCm software stack to use HIP.
Code Adaptations
Use Target.CurrentGPU throughout the test suite where it is required
Use Target.is_gpu() to detect GPU targets in all places where previously targets were checked only against Target.CUDA
Documentation
Adapt installation guide and GPU codegen guide to reflect the new target
Adapt backend GPU codegen docs
Extend contrib guide with info on GPU development
On CI Testing
Since cupy only supports the combinations NVidia+CUDA and ROCm+HIP, we cannot currently test HIP code generation in the CI since we don't have GitLab runners with AMD GPUs.
Rationale
Separate modelling of CUDA and HIP in the code generator will be necessary to capture architectural differences in !438 (merged).
Also, it turns out to be very important for the new waLBerla code generator (see https://i10git.cs.fau.de/da15siwa/sfg-walberla);
using the target is the easiest way to distinguish between CUDA and HIP for GPU codegen.
