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Comment: update performance

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$ numactl --physcpubind=0-17 --membind=0 python …


CategoryModelLatency batchsize=1 (ms, small is better)Throughput batchsize=128 (fps,
higher
big is better)

no mkldnn

release 1.3 + mkldnn

speedup

no mkldnn

w/o MKL-DNNw/ MKL-DNNspeedupw/o MKL-DNNw/ MKL-DNN
release 1.3 + mkldnn
speedup
CNN/classificationResNet-50 v197.19
18
13.
94
04
5
7.
13
4510.29
132
163.
05
52
12
15.
84
90
ResNet-50 v298.69
18
13.
93
02
5
7.
21
589.94
127
154.17
12
15.
79
51
Inception v3175.17
26
16.
34
77
6
10.
65
445.74
110
135.
00
33
19
23.
16
57
Inception v4330.93
66
31.
96
40
4
10.
94
543.04
59
69.
28
60
19
22.
47
87
DenseNet111.66
53
18.
31
90
2
5.
09
918.52
121
149.
79
88
14
17.
30
60
MobileNet38.56
7
4.
32
42
5
8.
27
7324.87
380
512.
54
25
15
20.
30
60
VGG16406.50
40
20.
08
07
10
20.
14
252.91
69
70.84
23
24.
96
31
AlexNet64.60
4
3.
33
80
14
17.
90
0026.58
689
965.
86
20
25
36.
96
32
inception-resnet v2181.10
111
49.
28
40
1
3.
63
675.48
69
82.
39
97
12
15.
66
14
CNN/object detectionFaster R-CNN1175.74
95
118.
15
62
12
9.
36
910.85
10
8.
51
57
12
10.
36
08
SSD-VGG16721.03
127
47.
48
62
5
15.
66
141.43（batchsize=224)
27
28.
35
90(batchsize=224)19.13
SSD-MobileNet
 239
239.40
100
28.
75
33
 2
8.
39
45
 4
4.07(batchsize=256)
57
69.
73
97(batchsize=256)14.
18 
18
RNNGNMT683.43
100
94.
30
00
6
7.
81
271.46(batchsize=64)
9
10.
97
63(batchsize=64)6.83
GANDCGAN8.940.
22
24
41
37.
36
85109.13
4059
4249.
74
36
37
38.
20
94

Inference Accuracy

The model is from gluon model zoo by pre-trained parameters. The top1 and top5 accuracy are verified by MKL-DNN backend. 

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