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85 lines
2.0 KiB
Python
85 lines
2.0 KiB
Python
#Multi GPU Basic example
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'''
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This tutorial requires your machine to have 2 GPUs
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"/cpu:0": The CPU of your machine.
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"/gpu:0": The first GPU of your machine
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"/gpu:1": The second GPU of your machine
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'''
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import numpy as np
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import tensorflow as tf
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import datetime
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#Processing Units logs
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log_device_placement = True
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#num of multiplications to perform
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n = 10
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'''
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Example: compute A^n + B^n on 2 GPUs
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Results on 8 cores with 2 GTX-980:
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* Single GPU computation time: 0:00:11.277449
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* Multi GPU computation time: 0:00:07.131701
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'''
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#Create random large matrix
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A = np.random.rand(1e4, 1e4).astype('float32')
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B = np.random.rand(1e4, 1e4).astype('float32')
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# Creates a graph to store results
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c1 = []
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c2 = []
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def matpow(M, n):
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if n < 1: #Abstract cases where n < 1
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return M
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else:
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return tf.matmul(M, matpow(M, n-1))
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'''
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Single GPU computing
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'''
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with tf.device('/gpu:0'):
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a = tf.constant(A)
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b = tf.constant(B)
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#compute A^n and B^n and store results in c1
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c1.append(matpow(a, n))
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c1.append(matpow(b, n))
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with tf.device('/cpu:0'):
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sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n
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t1_1 = datetime.datetime.now()
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with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:
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# Runs the op.
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sess.run(sum)
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t2_1 = datetime.datetime.now()
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'''
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Multi GPU computing
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'''
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#GPU:0 computes A^n
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with tf.device('/gpu:0'):
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#compute A^n and store result in c2
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a = tf.constant(A)
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c2.append(matpow(a, n))
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#GPU:1 computes B^n
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with tf.device('/gpu:1'):
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#compute B^n and store result in c2
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b = tf.constant(B)
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c2.append(matpow(b, n))
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with tf.device('/cpu:0'):
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sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n
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t1_2 = datetime.datetime.now()
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with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:
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# Runs the op.
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sess.run(sum)
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t2_2 = datetime.datetime.now()
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print "Single GPU computation time: " + str(t2_1-t1_1)
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print "Multi GPU computation time: " + str(t2_2-t1_2) |