I am using tensorflow to build `cnn net` in image classification experiment，I found such phenomenon as:

operation 1：`tf.nn.conv2d(x, [3,3,32,32], strides=[1,1,1,1], padding='SAME')`

the shape of x is `[128,128,32]`，means convolution using 3x3 kernel on x，both input channels and output channels are 32，the total multiply times is 

```
3*3*32*32*128*128=150994944

```

operation 2：`tf.nn.conv2d(x, [3,3,64,64], strides=[1,1,1,1], padding='SAME')`

the shape of x is `[64,64,64]`，means convolution using 3x3 kernel on x，both input channels and output channels are 64，the total multiply times is

```
3*3*64*64*64*64=150994944

```

In contrast with operation 1，the feature map size of operation 2 scale down to 1/2 and the channel number doubled. The multiply times are the same so the running time should be same.But in practice the running time of operation 1 is longer than operation 2. 

My measure method was shown below

1. eliminate an convolution of operation 1，the training time for one epoch reduced 23 seconds，means the running time of operation 1 is 23 seconds.
2. eliminate an convolution of operation 2，the training time for one epoch reduced 13 seconds，means the running time of operation 2 is 13 seconds.

the phenomenon can reproduction every time。
My gpu is nvidia `gtx980Ti`，os is ubuntu `16.04`。

So that comes the question: Why the running time of operation 1 was longer than operation 2?