add graph in IntelOptimizedReadme

benchmark/IntelOptimizedPaddle.md

@@ -2,21 +2,17 @@
 
 Machine:
 
-- Server
+- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
- 	- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
+- Laptop: TBD
-- Laptop
- 	- DELL XPS15-9560-R1745: i7-7700HQ 8G 256GSSD
- 	- i5 MacBook Pro (Retina, 13-inch, Early 2015)
-- Desktop
- 	- i7-6700k
 
 System: CentOS release 6.3 (Final), Docker 1.12.1.
 
-PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)
+PaddlePaddle: (TODO: will rerun after 0.11.0)
+- paddlepaddle/paddle:latest (for MKLML and MKL-DNN)
   - MKL-DNN tag v0.11
   - MKLML 2018.0.1.20171007
+- paddlepaddle/paddle:latest-openblas (for OpenBLAS)
   - OpenBLAS v0.2.20
-(TODO: will rerun after 0.11.0)
 	 
 On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
 
@@ -35,9 +31,7 @@ Input image size - 3 * 224 * 224, Time: images/second
 | MKLML        | 12.12 | 13.70 | 16.18  |
 | MKL-DNN      | 28.46 | 29.83 | 30.44  |
 
-
+<img src="figs/vgg-cpu-train.png" width="500">
-chart on batch size 128
-TBD
 
  - ResNet-50
 
@@ -47,9 +41,7 @@ TBD
 | MKLML        | 32.52 | 31.89 | 33.12  |
 | MKL-DNN      | 81.69 | 82.35 | 84.08  |
 
-
+<img src="figs/resnet-cpu-train.png" width="500">
-chart on batch size 128
-TBD
 
  - GoogLeNet
 
@@ -59,10 +51,7 @@ TBD
 | MKLML        | 128.46| 137.89| 158.63 |
 | MKL-DNN      | 250.46| 264.83| 269.50 |
 
-chart on batch size 128
+<img src="figs/googlenet-cpu-train.png" width="500">
-TBD
 
 ### Laptop
 TBD
-### Desktop
-TBD