[Python] Cupy is slower than numpy

I tried to speed up my python code with cupy instead of numpy. The problem here is, that using cupy, my code got drastically slower. Maybe I went a little bit to naive on that problem.

Maybe anyone can find a bottleneck in my code:

import cupy as np
import time as ti

def f(y, t):
y_ = np.zeros(2 * N_1*N_2) # n: e-6, c: e-5
for i in range(0, N_1*N_2):
y_ = y[i + N_1*N_2] # n: e-7, c: e-5 or e-6
for i in range(N_1*N_2):
sum = -4*y # n: e-7, c: e-7 after some statements e-5
if (i + 1 in indexes) and (not (i in indi)):
sum += y[i+1] # n: e-7, c: e-7 after some statements e-5
if (i - 1) in indexes and (i % N_1 != 0):
sum += y[i-1] # n: e-7, c: e-7 after some statements e-5
if i + N_1 in indexes:
sum += y[i+N_1] # n: e-7, c: e-7 after some statements e-5
if i - N_1 in indexes:
sum += y[i-N_1] # n: e-7, c: e-7 after some statements e-5
y_[i + N_1*N_2] = sum

return y_

def k_1(y, t, h):
return np.asarray(f(y, t)) * h

def k_2(y, t, h):
return np.asarray(f(np.add(np.asarray(y) , np.multiply(1/2 , k_1(y, t, h))), t + 1/2 * h)) * h

# k_2, k_4 look just like k_2, may be with an 1/2 here or there

# some init stuff is happening here

while t < T_end:
# also some magic happening here which is just data saving
y = np.asarray(y) + 1/6*(k_1(y, t, m) + 2*k_2(y, t, m) + 2*k_3(y, t, m) + k_4(y, t, m))
t += m


EDIT I tried to benchmark my code and here are some results they can be seen as a comment in the code. Each number stays for one line. The units are seconds. n: Numpy, c:CuPy, i mostly give a rough estimate of the order. Additional i tested

np.multiply # n: e-6, c: e-5


and

np.add # n: e-5 or e-6, c: 0.005 or e-5

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