added basic operations with multi GPU

multigpu_basics.py

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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)