How do Numba and CuPy handle memory management differently?

How do Numba and CuPy handle memory management differently?

Numba uses a just-in-time compilation approach, optimizing memory usage at runtime, while CuPy mimics NumPy's API and manages memory on the GPU directly.

Which one is more efficient for large datasets?

CuPy is generally more efficient for large datasets on GPUs due to its direct memory management, but Numba can be faster for CPU-bound tasks.

Can I use both Numba and CuPy together?

Yes, you can use Numba to accelerate CPU computations and CuPy for GPU operations, allowing for a hybrid approach.

What are the main advantages of using CuPy over Numba?

CuPy offers better performance for GPU operations and is more compatible with existing NumPy code, making it easier to transition.

Are there any limitations to using Numba?

Yes, Numba has limitations with certain Python features and may not support all NumPy functions, which can restrict its use.