Fundamental GPU Support

This MR introduces the fundamentals of GPU support to the new backend

General

• Introduce GenericGpu platform and threads range export: GPU platforms communicate the kernel's required thread grid size to the outside via a GpuThreadsRange object separate from the AST
• Add configuration options relating to GPUs

CUDA Platform

• Introduce CUDA platform
• Add materialization + guards for full and sparse iteration spaces
• Add materialization of math functions

SYCL Platform

• Introduce SYCL platform
• Add materialization + guards for full and sparse iteration spaces
• Add materialization of math functions

CUDA Just-In-Time Compiler

• Migrate implementation of cupy-based JIT to new backend as an object-oriented structure

Deviations and Missing Features

In the new implementation, block size selection is entirely up to the JIT / the runtime system and no longer affects the backend. Adaptive block sizes, register restrictions, etc. are not yet implemented by this MR.

docs/source/api/kernelcreation/index.rst

@@ -4,10 +4,15 @@ Kernel Creation
 
 The primary interface for creating numerical kernels in pystencils is the function `create_kernel`.
 
+Configuration
+=============
 
-.. autoclass:: pystencils.CreateKernelConfig
+.. automodule:: pystencils.config
     :members:
 
+Creation
+========
+
 .. autofunction:: pystencils.create_kernel
 
 .. autoclass:: pystencils.backend.KernelFunction