Tensorflow Basic performance comparison (CPU vs GPU vs Multi-GPU) based on https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/5_MultiGPU/multigpu_basics.py

tf_cmp_basic_multi_gpu.py

from __future__ import print_function
'''
Basic Multi GPU computation example using TensorFlow library.
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

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

# Create 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 CPU computing
'''
with tf.device('/cpu:0'):
    a = tf.placeholder(tf.float32, [10000, 10000])
    b = tf.placeholder(tf.float32, [10000, 10000])
    # 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_0 = datetime.datetime.now()
with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:
    # Run the op.
    sess.run(sum, {a:A, b:B})
t2_0 = datetime.datetime.now()

'''
Single GPU computing
'''
with tf.device('/gpu:0'):
    a = tf.placeholder(tf.float32, [10000, 10000])
    b = tf.placeholder(tf.float32, [10000, 10000])
    # 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:
    # Run the op.
    sess.run(sum, {a:A, b:B})
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.placeholder(tf.float32, [10000, 10000])
    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.placeholder(tf.float32, [10000, 10000])
    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:
    # Run the op.
    sess.run(sum, {a:A, b:B})
t2_2 = datetime.datetime.now()

print("Single CPU computation time: " + str(t2_0-t1_0))
print("Single GPU computation time: " + str(t2_1-t1_1))
print("Multi GPU computation time: " + str(t2_2-t1_2))