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#Multi GPU Basic example

'''

This tutorial requires your machine to have 2 GPUs

"/cpu:0": The CPU of your machine.

"/gpu:0": The first GPU of your machine

"/gpu:1": The second GPU of your machine

'''

import numpy as np

import tensorflow as tf

import datetime

#Processing Units logs

log_device_placement = True

#num of multiplications to perform

n = 10

'''

Example: compute A^n + B^n on 2 GPUs

Results on 8 cores with 2 GTX-980:

* Single GPU computation time: 0:00:11.277449

* Multi GPU computation time: 0:00:07.131701

'''

#Create random large matrix

A = np.random.rand(1e4, 1e4).astype('float32')

B = np.random.rand(1e4, 1e4).astype('float32')

# Creates a graph to store results

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))

'''

Single GPU computing

'''

with tf.device('/gpu:0'):

a = tf.constant(A)

b = tf.constant(B)

#compute A^n and B^n and store results in c1

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:

# Runs the op.

sess.run(sum)

t2_1 = datetime.datetime.now()

'''

Multi GPU computing

'''

#GPU:0 computes A^n

with tf.device('/gpu:0'):

#compute A^n and store result in c2

a = tf.constant(A)

c2.append(matpow(a, n))

#GPU:1 computes B^n

with tf.device('/gpu:1'):

#compute B^n and store result in c2

b = tf.constant(B)

c2.append(matpow(b, n))

with tf.device('/cpu:0'):

sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n

t1_2 = datetime.datetime.now()

with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:

# Runs the op.

sess.run(sum)

t2_2 = datetime.datetime.now()

print "Single GPU computation time: " + str(t2_1-t1_1)

print "Multi GPU computation time: " + str(t2_2-t1_2)