NumPy vs CuPy: Which one should I use for GPU computing?

What are the main differences between Numba and CuPy?

Numba is a just-in-time compiler for Python that translates a subset of Python and NumPy code into fast machine code. CuPy, on the other hand, is a library that implements NumPy-compatible multi-dimensional arrays on NVIDIA GPUs, allowing for GPU-accelerated computing.

When should I choose Numba over CuPy?

You should choose Numba when you want to optimize CPU-bound code or when you have existing NumPy code that you want to speed up without rewriting it for GPU. It's great for functions that can benefit from JIT compilation.

Are there any performance benchmarks comparing the two?

Yes, benchmarks typically show that CuPy can significantly outperform Numba for large-scale array operations on GPUs, while Numba can be faster for smaller computations on CPUs. It really depends on the specific use case.

Can I use both Numba and CuPy in the same project?

Absolutely! You can use Numba for CPU optimizations and CuPy for GPU tasks within the same project, allowing you to leverage the strengths of both libraries.