I have the following routine aiming to calculate in parallel different SVD of random matrices:

import numpy as np 
from numba import jit,prange 

@jit(nopython=True,parallel=True)
def svd_bn(aa,n):
    
    res=[]
    for k in prange(n):
        u,s,v=np.linalg.svd(aa[k],0)
        res.append( s ) 
        
        
    return res
aa=[np.random.rand(2*k,2*(k+1)) for k in range(1,10)]

res=svd_bn(aa,len(aa))

On a Windows OS, this works fine and returns the sorted elements in the list. On a Linux OS, this leads to a “double free or corruption (!prev)” error. My suspicion is that the parallelization on Linux leads to a race condition that is avoided on Windows, plus the “res” list is changing its entries dynamically.

Is there a way to overcome this problem using Numba? Note that in principle we don’t know the shape of the u,s,v arrays returned from the routine.