ubuntu測試gpu性能的方法：

1.測試系統自動分配設備示例：

#-*- coding:utf-8 -*-
import tensorflow as tf
# 新建一個 graph.
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# 新建session with log_device_placement并設置為True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# 運行這個 op.
print(sess.run(c))

輸出如下:

Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus
id: 0000:05:00.0
b: /job:localhost/replica:0/task:0/gpu:0
a: /job:localhost/replica:0/task:0/gpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22. 28.]
[ 49. 64.]]

2.測試手動指定分配設備示例：

#-*- coding:utf-8 -*-
import tensorflow as tf
# 新建一個graph.
with tf.device('/cpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# 新建session with log_device_placement并設置為True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# 運行這個op.
print(sess.run(c))

a和b操作都被指派給了cpu:0，輸出如下:

Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: Tesla K40c, pci bus
id: 0000:05:00.0
b: /job:localhost/replica:0/task:0/cpu:0
a: /job:localhost/replica:0/task:0/cpu:0
MatMul: /job:localhost/replica:0/task:0/gpu:0
[[ 22. 28.]

[ 49. 64.]]