# Benchmark

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

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)
	 
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 6148 CPU @ 2.40GHz

Input image size - 3 * 224 * 224, Time: images/second

- VGG-19

| BatchSize    | 64    | 128  | 256     |
|--------------|-------| -----| --------|
| OpenBLAS     | 7.80  | 9.00  | 10.80  | 
| MKLML        | 12.12 | 13.70 | 16.18  |
| MKL-DNN      | 28.46 | 29.83 | 30.44  |


chart on batch size 128
TBD

 - ResNet-50

| BatchSize    | 64    | 128   | 256    |
|--------------|-------| ------| -------|
| OpenBLAS     | 25.22 | 25.68 | 27.12  | 
| MKLML        | 32.52 | 31.89 | 33.12  |
| MKL-DNN      | 81.69 | 82.35 | 84.08  |


chart on batch size 128
TBD

 - GoogLeNet

| BatchSize    | 64    | 128   | 256    |
|--------------|-------| ------| -------|
| OpenBLAS     | 88.58 | 92.15 | 101.4  | 
| MKLML        | 111.5 | 119.8 | 131.2  |
| MKL-DNN      | 238.0 | 259.6 | 276.6  |

chart on batch size 128
TBD

### Laptop
TBD
### Desktop
TBD