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This pull request addresses issue #28 by implementing a new feature in
Pyccel that allows users to define custom GPU kernels. The syntax for
creating these kernels is inspired by Numba. and I also need to fix
issue #45 for testing purposes
**Commit Summary**
- Introduced KernelCall class
- Added cuda printer methods _print_KernelCall and _print_FunctionDef to
generate the corresponding CUDA representation for both kernel calls and
definitions
- Added IndexedFunctionCall represents an indexed function call
- Added CUDA module and cuda.synchronize()
- Fixing a bug that I found in the header: it does not import the
necessary header for the used function
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Co-authored-by: EmilyBourne <[email protected]>
Co-authored-by: bauom <[email protected]>
Co-authored-by: Emily Bourne <[email protected]>
Pyccel now supports NVIDIA CUDA, empowering users to accelerate numerical computations on GPUs seamlessly. With Pyccel's high-level syntax and automatic code generation, harnessing the power of CUDA becomes effortless. This documentation provides a quick guide to enabling CUDA in Pyccel
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## Cuda Decorator
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### kernel
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The kernel decorator allows the user to declare a CUDA kernel. The kernel can be defined in Python, and the syntax is similar to that of Numba.
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