tensorflow GPU小测试

tensorflow GPU小测试

简单测试了一下tensorflow的GPU计算和CPU计算的区别。这里的计算例子只非常简单的小规模矩阵相乘，但是也体现出了CPU和GPU算力的差距，代码及结果如下：

import tensorflow as tfimport datetime#running# Creates a graph.(cpu version)print('cpu version')starttime1 = datetime.datetime.now()with tf.device('/gpu:0'):  a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[6, 9], name='a')  b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[9, 6], name='b')  c = tf.matmul(a, b)  c = tf.matmul(c,a)  c = tf.matmul(c,b)# Creates a session with log_device_placement set to True.sess1 = tf.Session(config=tf.ConfigProto(log_device_placement=True))# Runs the op.for i in range(59999):    sess1.run(c)print(sess1.run(c))sess1.close()endtime1 = datetime.datetime.now()time1 = (endtime1 - starttime1).microseconds#print('time1:',time1)#############################################print('gpuversion')# Creates a graph.(gpu version)starttime2 = datetime.datetime.now()#runningwith tf.device('/gpu:0'):  a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[6, 9], name='a')  b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[9, 6], name='b')  c = tf.matmul(a, b)  c = tf.matmul(c,a)  c = tf.matmul(c,b)# Creates a session with log_device_placement set to True.sess2 = tf.Session(config=tf.ConfigProto(log_device_placement=True))# Runs the op.for i in range(59999):    sess2.run(c)print(sess2.run(c))sess2.close()endtime2 = datetime.datetime.now()time2 = (endtime2 - starttime2).microsecondsprint('time1:',time1)print('time2:',time2)

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结果如下：

cpu version[[  18225.   36450.   54675.   72900.   91125.  109350.] [  24300.   48600.   72900.   97200.  121500.  145800.] [  18225.   36450.   54675.   72900.   91125.  109350.] [  24300.   48600.   72900.   97200.  121500.  145800.] [  18225.   36450.   54675.   72900.   91125.  109350.] [  24300.   48600.   72900.   97200.  121500.  145800.]]gpuversion[[  18225.   36450.   54675.   72900.   91125.  109350.] [  24300.   48600.   72900.   97200.  121500.  145800.] [  18225.   36450.   54675.   72900.   91125.  109350.] [  24300.   48600.   72900.   97200.  121500.  145800.] [  18225.   36450.   54675.   72900.   91125.  109350.] [  24300.   48600.   72900.   97200.  121500.  145800.]]time1: 356158time2: 363249

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注：以上时间单位是微秒