In this example, we provide a demonstration of the Numba compiler using the @guvectorize annotation.

The task to execute is matrix multiplication:

1. Import guvectorize from the numba library and the numpy module:

from numba import guvectorize 
import numpy as np 

2. Using the @guvectorize decorator, we define the matmul function, which will perform the matrix multiplication task:

@guvectorize(['void(int64[:,:], int64[:,:], int64[:,:])'], 
             '(m,n),(n,p)->(m,p)') 
def matmul(A, B, C): 
    m, n = A.shape 
    n, p = B.shape 
    for i in range(m): 
        for j in range(p): 
            C[i, j] = 0 
            for k in range(n): 
                C[i, j] += A[i, k] * B[k, j] 

3. The input matrices are 10 × 10 in size, while the elements are integers:

dim = 10 
A = np.random.randint(dim,size=(dim, dim)) 
B = np.random.randint(dim,size=(dim, dim)) 

4. Finally, we call the matmul function on the previously defined input matrices:

C = matmul(A, B) 

5. We print the input matrices and the resulting matrix:

print("INPUT MATRIX A") 
print(":
%s" % A) 
print("INPUT MATRIX B") 
print(":
%s" % B) 
print("RESULT MATRIX C = A*B") 
print(":
%s" % C)