How can I use Numba with CuPy for GPU acceleration?

How can I use Numba with CuPy for GPU acceleration?

You can use Numba to compile Python functions to run on the GPU with CuPy. First, ensure you have both libraries installed.

What are the benefits of using Numba with CuPy?

Using Numba with CuPy allows for just-in-time compilation, which can significantly speed up computations on large arrays.

Can you provide a simple code example?

Sure! Here's a basic example:

import cupy as cp
from numba import cuda

@cuda.jit
def add_arrays(a, b, c):
    idx = cuda.grid(1)
    if idx < c.size:
        c[idx] = a[idx] + b[idx]

# Initialize CuPy arrays
n = 1000000
x = cp.arange(n)
y = cp.arange(n)
z = cp.empty(n)

# Launch the kernel
add_arrays[(n + 255) // 256, 256](x, y, z)

Are there any limitations I should be aware of?

Yes, some limitations include the need for compatible data types and the fact that not all Python features are supported in Numba.