What are some examples comparing Numba and CuPy?

Can you give me some examples comparing Numba and CuPy?

Sure! Numba and CuPy are both libraries for GPU computing in Python, but they have different use cases. For example, Numba is great for just-in-time compilation of Python functions, while CuPy is more like NumPy but optimized for GPU. Would you like to see a specific code example?

Yes, please show me a simple example of matrix multiplication using both.

Here's a simple example:

Using Numba:

import numpy as np
from numba import jit

@jit
def matmul_numba(A, B):
    return np.dot(A, B)

Using CuPy:

import cupy as cp

A = cp.array([[1, 2], [3, 4]])
B = cp.array([[5, 6], [7, 8]])
result = cp.dot(A, B)

In this example, Numba compiles the function for CPU execution, while CuPy runs on the GPU.

What are the performance differences between them?

Generally, CuPy can outperform Numba for large-scale matrix operations because it leverages GPU acceleration. However, Numba can be faster for smaller tasks or when using CPU, especially if the overhead of transferring data to the GPU is considered.