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.

src/pystencils/backend/platforms/cuda.py

@@ -37,7 +37,7 @@ GRID_DIM = [
 class CudaPlatform(GenericGpu):
 
     def __init__(
-        self, ctx: KernelCreationContext, indexing_cfg: GpuIndexingConfig | None
+        self, ctx: KernelCreationContext, indexing_cfg: GpuIndexingConfig | None = None
     ) -> None:
         super().__init__(ctx)
         self._cfg = indexing_cfg if indexing_cfg is not None else GpuIndexingConfig()