Which is better for GPU programming: PyCUDA or Numba?

I'm trying to decide between PyCUDA and Numba for my GPU programming project. Which one should I choose?

Both PyCUDA and Numba are great options for GPU programming, but they serve slightly different purposes. PyCUDA gives you more control over CUDA kernels, while Numba is easier to use for Python developers.

What are the main advantages of using PyCUDA?

PyCUDA allows for fine-grained control over GPU resources and lets you write custom CUDA kernels in Python. It's great for performance-critical applications.

And what about Numba? What makes it appealing?

Numba is appealing because it allows you to accelerate existing Python code with minimal changes. It automatically compiles Python functions to machine code, making it user-friendly.

Are there any performance benchmarks comparing the two?

Yes, there are benchmarks available that show performance differences depending on the specific use case. Generally, PyCUDA can outperform Numba for highly optimized kernels, but Numba is often faster for simpler tasks.