Here is the full code for the previous example:

import numpy as np 
import tensorflow as tf 
import datetime 

log_device_placement = True 

n = 10 

A = np.random.rand(10000, 10000).astype('float32') 
B = np.random.rand(10000, 10000).astype('float32') 


c1 = [] 
c2 = [] 

def matpow(M, n): 
    if n < 1: #Abstract cases where n < 1 
        return M 
    else: 
        return tf.matmul(M, matpow(M, n-1)) 

with tf.device('/gpu:0'): 
    a = tf.placeholder(tf.float32, [10000, 10000]) 
    b = tf.placeholder(tf.float32, [10000, 10000]) 
    c1.append(matpow(a, n)) 
    c1.append(matpow(b, n)) 

with tf.device('/cpu:0'): 
  sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n 

t1_1 = datetime.datetime.now() 
with tf.Session(config=tf.ConfigProto 
              (log_device_placement=log_device_placement)) as sess: 
     sess.run(sum, {a:A, b:B}) 
t2_1 = datetime.datetime.now()