update the VGG benchmark on CentOs6.3 and Intel 6148

benchmark/IntelOptimizedPaddle.md

-# Benchmark
-
-Machine:
-
+# Benchmark
+
+Machine:
+
 - Server
- 	- Intel(R) Xeon(R) Gold 6148M 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
  	- DELL XPS15-9560-R1745: i7-7700HQ 8G 256GSSD
- 	- i5 MacBook Pro (Retina, 13-inch, Early 2015)
-- Desktop
- 	- i7-6700k
-
-System: CentOS 7.3.1611
-
-PaddlePaddle: commit cfa86a3f70cb5f2517a802f32f2c88d48ab4e0e0
-
+ 	- 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 (TODO: will rerun after 0.11.0)
+
 - MKL-DNN tag v0.10
 - MKLML 2018.0.20170720
-- OpenBLAS v0.2.20
-	 
-On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
-
-## Benchmark Model
-
-### Server
-Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148M CPU @ 2.40GHz
-
-Input image size - 3 * 224 * 224, Time: images/second
-
-- VGG-19
-
-| BatchSize    | 64    | 128  | 256     |
-|--------------|-------| -----| --------|
-| OpenBLAS     | 7.86  | 9.02  | 10.62  | 
-| MKLML        | 11.80 | 13.43 | 16.21  |
-| MKL-DNN      | 29.07 | 30.40 | 31.06  |
-
-
-chart on batch size 128
-TBD
-
+- OpenBLAS v0.2.20
+	 
+On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
+
+## Benchmark Model
+
+### Server
+Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148M CPU @ 2.40GHz
+
+Input image size - 3 * 224 * 224, Time: images/second
+
+- VGG-19
+
+| BatchSize    | 64    | 128  | 256     |
+|--------------|-------| -----| --------|
+| OpenBLAS     | 7.82  | 8.62  | 10.34  | 
+| MKLML        | 11.02 | 12.86 | 15.33  |
+| MKL-DNN      | 27.69 | 28.8 | 29.27  |
+
+
+chart on batch size 128
+TBD
+
  - ResNet
- - GoogLeNet
-
-### Laptop
-TBD
-### Desktop
-TBD
+ - GoogLeNet
+
+### Laptop
+TBD
+### Desktop
+TBD