diff --git a/benchmark/IntelOptimizedPaddle.md b/benchmark/IntelOptimizedPaddle.md
index 16c2390fd31bf1c79f29735fb98180d3f7302eb2..26930a76377397cc36af8cf411b49b02e4f67748 100644
--- a/benchmark/IntelOptimizedPaddle.md
+++ b/benchmark/IntelOptimizedPaddle.md
@@ -2,21 +2,17 @@
Machine:
-- Server
- - 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
+- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
+- Laptop: TBD
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)
-- MKL-DNN tag v0.11
-- MKLML 2018.0.1.20171007
-- OpenBLAS v0.2.20
-(TODO: will rerun after 0.11.0)
+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
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 |
-
-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 |
-
-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
-TBD
+
### Laptop
TBD
-### Desktop
-TBD
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